Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. METHOD: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. CONCLUSION: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g., magnetic resonance imaging, MRI). Due to computational resource constraints, most CAD methods focus on quantitative features in specific regions, neglecting the holistic nature of the images, which poses a challenge for a comprehensive understanding of pathological changes in AD. To address this issue, we propose a lightweight dual multi-level hybrid pyramid convolutional neural network (DMA-HPCNet) to aid clinical diagnosis of AD. Specifically, we introduced ResNet as the backbone network and modularly extended the hybrid pyramid convolution (HPC) block and the dual multi-level attention (DMA) module. Among them, the HPC block is designed to enhance the acquisition of information at different scales, and the DMA module is proposed to sequentially extract different local and global representations from the channel and spatial domains. Our proposed DMA-HPCNet method was evaluated on baseline MRI slices of 443 subjects from the ADNI dataset. Experimental results show that our proposed DMA-HPCNet model performs efficiently in AD-related classification tasks with low computational cost.

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images.

BACKGROUND AND OBJECTIVE: Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. METHODS: Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. RESULTS: The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. CONCLUSION: The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.

HAMMF: Hierarchical attention-based multi-task and multi-modal fusion model for computer-aided diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative condition, and early intervention can help slow its progression. However, integrating multi-dimensional information and deep convolutional networks increases the model parameters, affecting diagnosis accuracy and efficiency and hindering clinical diagnostic model deployment. Multi-modal neuroimaging can offer more precise diagnostic results, while multi-task modeling of classification and regression tasks can enhance the performance and stability of AD diagnosis. This study proposes a Hierarchical Attention-based Multi-task Multi-modal Fusion model (HAMMF) that leverages multi-modal neuroimaging data to concurrently learn AD classification tasks, cognitive score regression, and age regression tasks using attention-based techniques. Firstly, we preprocess MRI and PET image data to obtain two modal data, each containing distinct information. Next, we incorporate a novel Contextual Hierarchical Attention Module (CHAM) to aggregate multi-modal features. This module employs channel and spatial attention to extract fine-grained pathological features from unimodal image data across various dimensions. Using these attention mechanisms, the Transformer can effectively capture correlated features of multi-modal inputs. Lastly, we adopt multi-task learning in our model to investigate the influence of different variables on diagnosis, with a primary classification task and a secondary regression task for optimal multi-task prediction performance. Our experiments utilized MRI and PET images from 720 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results show that our proposed model achieves an overall accuracy of 93.15% for AD/NC recognition, and the visualization results demonstrate its strong pathological feature recognition performance.

Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.

BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. OBJECTIVES: This study develops advanced models to enhance the accuracy of diagnosis of NS. METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. CONCLUSION: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images.

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection's severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

An Ensemble Architecture for Melanoma Classification.

Melanoma represents an extremely aggressive type of skin lesion. Despite its high mortality rate, when detected in its initial stage, the projected five-year survival rate is notably high. The advancement of Artificial Intelligence in recent years has facilitated the creation of diverse solutions aimed at assisting medical diagnosis. This proposal presents an architecture for melanoma classification.

The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.

BACKGROUND: Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases. OBJECTIVE: The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs? METHOD: The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity). RESULTS: The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree). CONCLUSION: The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.

Automated thorax disease diagnosis using multi-branch residual attention network.

Chest X-ray (CXR) is an extensively utilized radiological modality for supporting the diagnosis of chest diseases. However, existing research approaches suffer from limitations in effectively integrating multi-scale CXR image features and are also hindered by imbalanced datasets. Therefore, there is a pressing need for further advancement in computer-aided diagnosis (CAD) of thoracic diseases. To tackle these challenges, we propose a multi-branch residual attention network (MBRANet) for thoracic disease diagnosis. MBRANet comprises three components. Firstly, to address the issue of inadequate extraction of spatial and positional information by the convolutional layer, a novel residual structure incorporating a coordinate attention (CA) module is proposed to extract features at multiple scales. Next, based on the concept of a Feature Pyramid Network (FPN), we perform multi-scale feature fusion in the following manner. Thirdly, we propose a novel Multi-Branch Feature Classifier (MFC) approach, which leverages the class-specific residual attention (CSRA) module for classification instead of relying solely on the fully connected layer. In addition, the designed BCEWithLabelSmoothing loss function improves the generalization ability and mitigates the problem of class imbalance by introducing a smoothing factor. We evaluated MBRANet on the ChestX-Ray14, CheXpert, MIMIC-CXR, and IU X-Ray datasets and achieved average AUCs of 0.841, 0.895, 0.805, and 0.745, respectively. Our method outperformed state-of-the-art baselines on these benchmark datasets.

Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer.

The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics-achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis.

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception_V3) 2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.

Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies.

Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (±1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (±2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance loss.

Advanced disk herniation computer aided diagnosis system.

Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to boost the performance of such algorithms even further, which include various transfer learning techniques, data augmentation, and feature concatenation. Normally, the use of these advanced techniques highly depends on the size and nature of the dataset being used. In the case of fine-grained medical image sets, which have subcategories within the main categories in the image set, there is a need to find the combination of the techniques that work the best on these types of images. In this work, we utilize these advanced techniques to find the best combinations to build a state-of-the-art lumber disc herniation computer-aided diagnosis system. We have evaluated the system extensively and the results show that the diagnosis system achieves an accuracy of 98% when it is compared with human diagnosis.

[Medical image segmentation data augmentation method based on channel weight and data-efficient features].

In computer-aided medical diagnosis, obtaining labeled medical image data is expensive, while there is a high demand for model interpretability. However, most deep learning models currently require a large amount of data and lack interpretability. To address these challenges, this paper proposes a novel data augmentation method for medical image segmentation. The uniqueness and advantages of this method lie in the utilization of gradient-weighted class activation mapping to extract data efficient features, which are then fused with the original image. Subsequently, a new channel weight feature extractor is constructed to learn the weights between different channels. This approach achieves non-destructive data augmentation effects, enhancing the model's performance, data efficiency, and interpretability. Applying the method of this paper to the Hyper-Kvasir dataset, the intersection over union (IoU) and Dice of the U-net were improved, respectively; and on the ISIC-Archive dataset, the IoU and Dice of the DeepLabV3+ were also improved respectively. Furthermore, even when the training data is reduced to 70 %, the proposed method can still achieve performance that is 95 % of that achieved with the entire dataset, indicating its good data efficiency. Moreover, the data-efficient features used in the method have interpretable information built-in, which enhances the interpretability of the model. The method has excellent universality, is plug-and-play, applicable to various segmentation methods, and does not require modification of the network structure, thus it is easy to integrate into existing medical image segmentation method, enhancing the convenience of future research and applications.

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach.

The tendency of skin diseases to manifest in a unique and yet similar appearance, absence of enough competent dermatologists, and urgency of diagnosis and classification on time and accurately, makes the need of machine aided diagnosis blatant. This study is conducted with the purpose of broadening the research in skin disease diagnosis with computer by traversing the capabilities of deep Learning algorithms to classify two skin diseases noticeably close in appearance, Psoriasis and Lichen Planus. The resemblance between these two skin diseases is striking, often resulting in their classification within the same category. Despite this, there is a dearth of research focusing specifically on these diseases. A customized 50 layers ResNet-50 architecture of convolutional neural network is used and the results are validated through fivefold cross-validation, threefold cross-validation, and random split. By utilizing advanced data augmentation and class balancing techniques, the diversity of the dataset has increased, and the dataset imbalance has been minimized. ResNet-50 has achieved an accuracy of 89.07%, sensitivity of 86.46%, and specificity of 86.02%. With their promising results, these algorithms make the potential of machine aided diagnosis clear. Deep Learning algorithms could provide assistance to physicians and dermatologists by classification of skin diseases, with similar appearance, in real-time.

Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study.

BACKGROUND: Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers. METHODS: Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level. RESULTS: 222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording. CONCLUSIONS: The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

Connectional-style-guided contextual representation learning for brain disease diagnosis.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval.

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI. An anchor-based WSI encoder is built to extract hierarchical region features and a prompt-based text encoder is introduced to learn fine-grained semantics from the diagnosis reports. The proposed framework is trained with a multivariate cross-modal loss function to learn semantic information from the diagnosis report at both the instance level and region level. After training, it can perform four types of retrieval tasks based on the multi-modal database to support diagnostic requirements. We conducted experiments on an in-house dataset and a public dataset to evaluate the proposed method. Extensive experiments have demonstrated the effectiveness of the proposed method and its advantages to the present histopathology retrieval methods. The code is available at https://github.com/hudingyi/FGCR.