Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

Daratumumab in Indian patients with relapsed and refractory multiple myeloma: a prospective, multicenter, phase IV study.

Aim: To assess the safety and effectiveness of daratumumab monotherapy in Indian patients with relapsed/refractory multiple myeloma. Methods: In this prospective, multicenter, phase IV study, patients (aged ≥18 years) received intravenous daratumumab (16 mg/kg) in six cycles. Safety was the primary end point. Results: Of the 139 patients included, 121 (87.1%) experienced ≥1 treatment-emergent adverse events (TEAEs; 53 [38.1%] drug-related), 32 (23%) had ≥1 serious TEAEs (five [3.6%] drug-related) and 16 (11.5%) deaths were reported (one death [0.7%] was drug-related). Overall response rate was 26.3%; 62.7% of patients had stable disease. Median time to first response and median progression-free survival were 5.2 and 5.9 months, respectively. Functional status and well-being were improved. Conclusion: Daratumumab showed an acceptable and expected safety profile with consistent efficacy, providing a novel therapeutic option for relapsed/refractory multiple myeloma management in India.

Daratumumab is a monoclonal antibody approved for the treatment of patients with relapsed/refractory multiple myeloma (RRMM). This study evaluated the outcome of daratumumab single therapy in Indian patients who were not cured with other drugs used for the same disease. 139 adult patients were included in this study from 15 institutes across India. Daratumumab (16 mg/kg) was diluted with 500 or 1000 ml of saline solution and given slowly through the intravenous route 16-times within 6 months. The study examined whether the safety profile and benefits of daratumumab reported in Indian patients were similar to those reported in the RRMM populations of other countries. The study found that most of the adverse events were not severe and could be easily treated by the study physician. 16 patients died (one might have been due to daratumumab treatment). Daratumumab treatment provided life support and recovery benefits to many patients. Daratumumab single therapy provides an appropriate and acceptable safety profile with no new adverse events and consistent benefits in RRMM patients. Clinical Trial Registration: NCT03768960 (ClinicalTrials.gov), CTRI/2019/06/019546.

Trait anxiety modulates the detection sensitivity of negative affect in speech: an online pilot study.

Acoustic perception of emotions in speech is relevant for humans to navigate the social environment optimally. While sensory perception is known to be influenced by ambient noise, and bodily internal states (e.g., emotional arousal and anxiety), their relationship to human auditory perception is relatively less understood. In a supervised, online pilot experiment sans the artificially controlled laboratory environment, we asked if the detection sensitivity of emotions conveyed by human speech-in-noise (acoustic signals) varies between individuals with relatively lower and higher levels of subclinical trait-anxiety, respectively. In a task, participants (n = 28) accurately discriminated the target emotion conveyed by the temporally unpredictable acoustic signals (signal to noise ratio = 10 dB), which were manipulated at four levels (Happy, Neutral, Fear, and Disgust). We calculated the empirical area under the curve (a measure of acoustic signal detection sensitivity) based on signal detection theory to answer our questions. A subset of individuals with High trait-anxiety relative to Low in the above sample showed significantly lower detection sensitivities to acoustic signals of negative emotions - Disgust and Fear and significantly lower detection sensitivities to acoustic signals when averaged across all emotions. The results from this pilot study with a small but statistically relevant sample size suggest that trait-anxiety levels influence the overall acoustic detection of speech-in-noise, especially those conveying threatening/negative affect. The findings are relevant for future research on acoustic perception anomalies underlying affective traits and disorders.

Visual Prompting based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

Inhibition of AKT Signaling Alters ßIV Spectrin Distribution at the AIS and Increases Neuronal Excitability.

The axon initial segment (AIS) is a highly regulated subcellular domain required for neuronal firing. Changes in the AIS protein composition and distribution are a form of structural plasticity, which powerfully regulates neuronal activity and may underlie several neuropsychiatric and neurodegenerative disorders. Despite its physiological and pathophysiological relevance, the signaling pathways mediating AIS protein distribution are still poorly studied. Here, we used confocal imaging and whole-cell patch clamp electrophysiology in primary hippocampal neurons to study how AIS protein composition and neuronal firing varied in response to selected kinase inhibitors targeting the AKT/GSK3 pathway, which has previously been shown to phosphorylate AIS proteins. Image-based features representing the cellular pattern distribution of the voltage-gated Na+ (Nav) channel, ankyrin G, ßIV spectrin, and the cell-adhesion molecule neurofascin were analyzed, revealing ßIV spectrin as the most sensitive AIS protein to AKT/GSK3 pathway inhibition. Within this pathway, inhibition of AKT by triciribine has the greatest effect on ßIV spectrin localization to the AIS and its subcellular distribution within neurons, a phenotype that Support Vector Machine classification was able to accurately distinguish from control. Treatment with triciribine also resulted in increased excitability in primary hippocampal neurons. Thus, perturbations to signaling mechanisms within the AKT pathway contribute to changes in ßIV spectrin distribution and neuronal firing that may be associated with neuropsychiatric and neurodegenerative disorders.

Multi-modal image sharpening in fourier transform infrared (FTIR) microscopy.

Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of heterogeneous samples. However, the optical resolution is fundamentally diffraction-limited, and therefore wavelength-dependent. This significantly limits resolution in infrared microscopy, which relies on long wavelengths (2.5 µm to 12.5 µm) for molecular specificity. The resolution is particularly restrictive in biomedical and materials applications, where molecular information is encoded in the fingerprint region (6 µm to 12 µm), limiting the maximum resolving power to between 3 µm and 6 µm. We present an unsupervised curvelet-based image fusion method that overcomes limitations in spatial resolution by augmenting infrared images with label-free visible microscopy. We demonstrate the effectiveness of this approach by fusing images of breast and ovarian tumor biopsies acquired using both infrared and dark-field microscopy. The proposed fusion algorithm generates a hyperspectral dataset that has both high spatial resolution and good molecular contrast. We validate this technique using multiple standard approaches and through comparisons to super-resolved experimentally measured photothermal spectroscopic images. We also propose a novel comparison method based on tissue classification accuracy.

Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification.

Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for hyperspectral image classification tasks. Training deep neural networks, such as a convolutional neural network for classification requires a large number of labeled samples. However, in remote sensing applications, we usually only have a small amount of labeled data for training because they are expensive to collect, although we still have abundant unlabeled data. In this paper, we propose semi-supervised deep learning for hyperspectral image classification-our approach uses limited labeled data and abundant unlabeled data to train a deep neural network. More specifically, we use deep convolutional recurrent neural networks (CRNN) for hyperspectral image classification by treating each hyperspectral pixel as a spectral sequence. In the proposed semi-supervised learning framework, the abundant unlabeled data are utilized with their pseudo labels (cluster labels). We propose to use all the training data together with their pseudo labels to pre-train a deep CRNN, and then fine-tune using the limited available labeled data. Further, to utilize spatial information in the hyperspectral images, we propose a constrained Dirichlet process mixture model (C-DPMM), a non-parametric Bayesian clustering algorithm, for semi-supervised clustering which includes pairwise must-link and cannot-link constraints-this produces high-quality pseudo-labels, resulting in improved initialization of the deep neural network. We also derived a variational inference model for the C-DPMM for efficient inference. Experimental results with real hyperspectral image data sets demonstrate that the proposed semi-supervised method outperforms state-of-the-art supervised and semi-supervised learning methods for hyperspectral classification.

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging.

Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals.

With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm. The region of importance (ROI) is identified during training the MKL for classification. The efficacy of the proposed method is validated by classifying different movement intentions from two subjects-an able-bodied and a spinal cord injury (SCI) subject. The preliminary results demonstrate that frontal and fronto-central regions are the most important regions for the tested subjects performing gait movements, which is consistent with the brain regions hypothesized to be involved in the control of lower-limb movements. However, we observed some regional changes comparing the able-bodied and the SCI subject. Moreover, in the longitudinal experiments, our findings exhibit the cortical plasticity triggered by the BMI use, as the classification accuracy and the weights for important regions-in sensor space-generally increased, as the user learned to control the exoskeleton for movement over multiple sessions.

Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution.

Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1-4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function.

High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton.

Brain-Machine Interface (BMI) systems allow users to control external mechanical systems using their thoughts. Commonly used in literature are invasive techniques to acquire brain signals and decode user's attempted motions to drive these systems (e.g. a robotic manipulator). In this work we use a lower-body exoskeleton and measure the users brain activity using non-invasive electroencephalography (EEG). The main focus of this study is to decode a paraplegic subject's motion intentions and provide him with the ability of walking with a lower-body exoskeleton accordingly. We present our novel method of decoding with high offline evaluation accuracies (around 98%), our closed loop implementation structure with considerably short on-site training time (around 38 sec), and preliminary results from the real-time closed loop implementation (NeuroRex) with a paraplegic test subject.

Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction.

Recent studies have demonstrated decoding of lower extremity limb kinematics from noninvasive electroencephalography (EEG), showing feasibility for development of an EEG-based brain-machine interface (BMI) to restore mobility following paralysis. Here, we present a new technique that preserves the statistical richness of EEG data to classify movement state from time-embedded low frequency EEG signals. We tested this new classifier, using cross-validation procedures, during sit-to-stand and stand-to-sit activity in 10 subjects and found decoding accuracy of greater than 95% on average. These results suggest that this classification technique could be used in a BMI system that, when combined with a robotic exoskeleton, can restore functional movement to individuals with paralysis.

A multi-classifier and decision fusion framework for robust classification of mammographic masses.

Most end-to-end Computer Aided Diagnosis (CAD) systems follow a three step approach - (1) Image enhancement and segmentation, (2) Feature extraction, and, (3) Classification. While the state of the art in image enhancement and segmentation can now very accurately identify regions of interest for feature extraction, they typically result in very high dimensional feature spaces. This adversely affects the performance of classification systems because a large feature space dimensionality necessitates a large training database to accurately model the statistics of class features (e.g. benign versus malignant classes). In this work, we present a robust multi-classifier decision fusion framework that employs a divide-and-conquer approach for alleviating the affects of high dimensionality of feature vectors. The feature space is partitioned into multiple smaller sized spaces, and a bank of classifiers (a multi-classifier system) is employed to perform classification in each of the partition. Finally, a decision fusion system merges decisions from each classifier in the bank into a single decision. The system is applied to the problem of classifying digital mammographic masses as either benign or malignant.