PS5-Net: a medical image segmentation network with multiscale resolution.

Purpose: In recent years, the continuous advancement of convolutional neural networks (CNNs) has led to the widespread integration of deep neural networks as a mainstream approach in clinical diagnostic support. Particularly, the utilization of CNN-based medical image segmentation has delivered favorable outcomes for aiding clinical diagnosis. Within this realm, network architectures based on the U-shaped structure and incorporating skip connections, along with their diverse derivatives, have gained extensive utilization across various medical image segmentation tasks. Nonetheless, two primary challenges persist. First, certain organs or tissues present considerable complexity, substantial morphological variations, and size discrepancies, posing significant challenges for achieving highly accurate segmentation. Second, the predominant focus of current deep neural networks on single-resolution feature extraction limits the effective extraction of feature information from complex medical images, thereby contributing to information loss via continuous pooling operations and contextual information interaction constraints within the U-shaped structure. Approach: We proposed a five-layer pyramid segmentation network (PS5-Net), a multiscale segmentation network with diverse resolutions that is founded on the U-Net architecture. Initially, this network effectively leverages the distinct features of images at varying resolutions across different dimensions, departing from prior single-resolution feature extraction methods to adapt to intricate and variable segmentation scenarios. Subsequently, to comprehensively integrate feature information from diverse resolutions, a kernel selection module is proposed to assign weights to features across different dimensions, enhancing the fusion of feature information from various resolutions. Within the feature extraction network denoted as PS-UNet, we preserve the classical structure of the traditional U-Net while enhancing it through the incorporation of dilated convolutions. Results: PS5-Net attains a Dice score of 0.9613 for liver segmentation on the CHLISC dataset and 0.8587 on the ISIC2018 dataset for skin lesion segmentation. Comparative analysis with diverse medical image segmentation methodologies in recent years reveals that PS5-Net has achieved the highest scores and substantial advancements. Conclusions: PS5-Net effectively harnesses the rich semantic information available at different resolutions, facilitating a comprehensive and nuanced understanding of the input medical images. By capitalizing on global contextual connections, the network adeptly captures the intricate interplay of features and dependencies across the entire image, resulting in more accurate and robust segmentation outcomes. The experimental validation of PS5-Net underscores its superior performance in medical image segmentation tasks, offering promising prospects for enhancing diagnostic and analytical processes within clinical settings. These results highlight the potential of PS5-Net to significantly contribute to the advancement of medical imaging technologies and ultimately improve patient care through more precise and reliable image analysis.

Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction.

OBJECTIVE: The study aims to investigate whether machine learning-based predictive models for cardiovascular disease (CVD) risk assessment show equivalent performance across demographic groups (such as race and gender) and if bias mitigation methods can reduce any bias present in the models. This is important as systematic bias may be introduced when collecting and preprocessing health data, which could affect the performance of the models on certain demographic sub-cohorts. The study is to investigate this using electronic health records data and various machine learning models. METHODS: The study used large de-identified Electronic Health Records data from Vanderbilt University Medical Center. Machine learning (ML) algorithms including logistic regression, random forest, gradient-boosting trees, and long short-term memory were applied to build multiple predictive models. Model bias and fairness were evaluated using equal opportunity difference (EOD, 0 indicates fairness) and disparate impact (DI, 1 indicates fairness). In our study, we also evaluated the fairness of a non-ML baseline model, the American Heart Association (AHA) Pooled Cohort Risk Equations (PCEs). Moreover, we compared the performance of three different de-biasing methods: removing protected attributes (e.g., race and gender), resampling the imbalanced training dataset by sample size, and resampling by the proportion of people with CVD outcomes. RESULTS: The study cohort included 109,490 individuals (mean [SD] age 47.4 [14.7] years; 64.5% female; 86.3% White; 13.7% Black). The experimental results suggested that most ML models had smaller EOD and DI than PCEs. For ML models, the mean EOD ranged from -0.001 to 0.018 and the mean DI ranged from 1.037 to 1.094 across race groups. There was a larger EOD and DI across gender groups, with EOD ranging from 0.131 to 0.136 and DI ranging from 1.535 to 1.587. For debiasing methods, removing protected attributes didn't significantly reduced the bias for most ML models. Resampling by sample size also didn't consistently decrease bias. Resampling by case proportion reduced the EOD and DI for gender groups but slightly reduced accuracy in many cases. CONCLUSIONS: Among the VUMC cohort, both PCEs and ML models were biased against women, suggesting the need to investigate and correct gender disparities in CVD risk prediction. Resampling by proportion reduced the bias for gender groups but not for race groups.

Transfer Learning of the ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning.

OBJECTIVE: The aim of the study was to verify the ability of the deep learning model to identify five subtypes and normal images in non-contrast enhancement CT of intracranial hemorrhage. METHODS: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) who underwent intracranial hemorrhage noncontrast enhanced CT were selected, obtaining 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. RESULTS: The overall accuracy of ResNet-18 and DenseNet-121 models was obtained as 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of the DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76, respectively. The AUC values of the two deep learning models were found to be above 0.9. CONCLUSION: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

Spatiotemporal characteristics analysis and potential distribution prediction of peste des petits ruminants (PPR) in China from 2007-2018.

Peste des petits ruminants (PPR) is a highly infectious disease that mainly infects small ruminants. To date, PPR has been confirmed in more than 70 countries. In China, PPR has occurred in more than 20 provinces and cities. In this study, based on geographic information system (GIS), spatial analysis was used to examine the occurrence of PPR in China from 2007 to 2018. The results showed that PPR first occurred in Tibet and gradually spread to other provinces. The outbreaks of PPR were concentrated in 2014, 2015 and 2018. Combining climate factors with the maximum entropy (MaxEnt), the results also suggested that the potential risk areas of PPR outbreaks in China were mainly Jiangsu, Yunnan and Anhui in Southeast China. Finally, a phylogenetic tree was used to analyse the evolutionary relationship between the peste des petits ruminants virus (PPRV) in China and the global ones, and it was found that the one in China had a close genetic relationship with the one in Mongolia, India and Bangladesh. Understanding and forecasting the distribution of PPR in China will help policymakers develop targeted monitoring plans. Likewise, analysing the global PPRV epidemic trends will play an important role in the elimination and prevention of PPR.

Therapeutic Suppression of miR-4261 Attenuates Colorectal Cancer by Targeting MCC.

The mutated in colorectal cancer (MCC) gene is an important colorectal tumor suppressor gene, although few studies have reported the microRNA(s) that could directly target MCC in colorectal cancer. Here, we used microRNA (miRNA) target prediction algorithms, and previously reported microarray data in human colorectal cancer found that only miR-4261 was predicted by all three databases to directly target MCC. Based on specimens from our own cohort of colorectal cancer patients, we further demonstrated that miR-4261 was overexpressed in colorectal cancer. Interestingly, overexpression of miR-4261 could enhance cell proliferation and G1/S phase transition of cell cycle, and promote cell migration in HCT116 and HT29 cells, while inhibition of miR-4261 had opposite effects. Luciferase reporter assay and western blot analysis confirmed MCC as a direct target of miR-4261. MCC small interfering RNA (siRNA) could abolish the suppressive effects of miR-4261 inhibitor on cell proliferation and migration in HCT116 and HT29 cell lines. Finally, we showed that therapeutic intervention with lentivirus-based miR-4261 sponge injection could effectively reduce tumor growth and inhibit cell proliferation in colorectal cancer xenograft. Collectively, our study is the first one to unravel the functional role of miR-4261, and it provides strong evidence that inhibition of miR-4261 through targeting of MCC might exert a therapeutic effect for colorectal cancer.