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Colonoscopy screening is an effective method to find and remove colon polyps before they can develop into colorectal cancer (CRC). Current follow-up recommendations, as outlined by the U.S. Multi-Society Task Force for individuals found to have polyps, primarily rely on histopathological characteristics, neglecting other significant CRC risk factors. Moreover, the considerable variability in colorectal polyp characterization among pathologists poses challenges in effective colonoscopy follow-up or surveillance. The evolution of digital pathology and recent advancements in deep learning provide a unique opportunity to investigate the added benefits of including the additional medical record information and automatic processing of pathology slides using computer vision techniques in the calculation of future CRC risk. Leveraging the New Hampshire Colonoscopy Registry's extensive dataset, many with longitudinal colonoscopy follow-up information, we adapted our recently developed transformer-based model for histopathology image analysis in 5-year CRC risk prediction. Additionally, we investigated various multimodal fusion techniques, combining medical record information with deep learning derived risk estimates. Our findings reveal that training a transformer model to predict intermediate clinical variables contributes to enhancing 5-year CRC risk prediction performance, with an AUC of 0.630 comparing to direct prediction. Furthermore, the fusion of imaging and non-imaging features, while not requiring manual inspection of microscopy images, demonstrates improved predictive capabilities for 5-year CRC risk comparing to variables extracted from colonoscopy procedure and microscopy findings. This study signifies the potential of integrating diverse data sources and advanced computational techniques in transforming the accuracy and effectiveness of future CRC risk assessments.

Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their decisions has become apparent in many applications. To that end, many interpretability methods that help explain the decision processes of ML models have been developed. Yet, there currently exists no widely-accepted metric to evaluate the quality of explanations generated by these methods. As a result, there currently is no standard way of measuring to what degree an interpretability method achieves an intended objective. Moreover, there is no accepted standard of performance by which we can compare and rank the current existing interpretability methods. In this paper, we propose a novel metric for quantifying the quality of explanations generated by interpretability methods. We compute the metric on three NLP tasks using six interpretability methods and present our results.

Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision. In addition to desirable evaluation metrics, a high level of interpretability is often required for these models to be reliably utilized. Therefore, explanations that offer insight into the process by which a model maps its inputs onto its outputs are much sought-after. Unfortunately, the current black box nature of machine learning models is still an unresolved issue and this very nature prevents researchers from learning and providing explicative descriptions for a model's behavior and final predictions. In this work, we propose a novel framework utilizing Adversarial Inverse Reinforcement Learning that can provide global explanations for decisions made by a Reinforcement Learning model and capture intuitive tendencies that the model follows by summarizing the model's decision-making process., Comment: Paper accepted to ICPR 2022

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes. Each year, more than 1 million diabetic patients undergo amputation due to failure to recognize DFU and get the proper treatment from clinicians. There is an urgent need to use a CAD system for the detection of DFU. In this paper, we propose using deep learning methods (EfficientDet Architectures) for the detection of DFU in the DFUC2020 challenge dataset, which consists of 4,500 DFU images. We further refined the EfficientDet architecture to avoid false negative and false positive predictions. The code for this method is available at https://github.com/Manugoyal12345/Yet-Another-EfficientDet-Pytorch., Comment: 8 Pages and DFUC Challenge

Machine learning methods have recently achieved high-performance in biomedical text analysis. However, a major bottleneck in the widespread application of these methods is obtaining the required large amounts of annotated training data, which is resource intensive and time consuming. Recent progress in self-supervised learning has shown promise in leveraging large text corpora without explicit annotations. In this work, we built a self-supervised contextual language representation model using BERT, a deep bidirectional transformer architecture, to identify radiology reports requiring prompt communication to the referring physicians. We pre-trained the BERT model on a large unlabeled corpus of radiology reports and used the resulting contextual representations in a final text classifier for communication urgency. Our model achieved a precision of 97.0%, recall of 93.3%, and F-measure of 95.1% on an independent test set in identifying radiology reports for prompt communication, and significantly outperformed the previous state-of-the-art model based on word2vec representations., Comment: Accepted in AMIA 2020 Informatics Summit

In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weighted MRI scans for chronic stroke patients. A total of 239 T1-weighted MRI scans of chronic ischemic stroke patients from a public dataset were retrospectively analyzed by 3D deep convolutional segmentation models with residual learning, using a novel zoom-in&out strategy. Dice similarity coefficient (DSC), Average symmetric surface distance (ASSD), and Hausdorff distance (HD) of the identified lesions were measured by using the manual tracing of lesions as the reference standard. Bootstrapping was employed for all metrics to estimate 95% confidence intervals. The models were assessed on the test set of 31 scans. The average DSC was 0.64 (0.51-0.76) with a median of 0.78. ASSD and HD were 3.6 mm (1.7-6.2 mm) and 20.4 mm (10.0-33.3 mm), respectively. To the best of our knowledge, this performance is the highest achieved on this public dataset. The latest deep learning architecture and techniques were applied for 3D segmentation on MRI scans and demonstrated to be effective for volumetric segmentation of chronic ischemic stroke lesions.

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients' electronic medical records. We used subsequent colonoscopy reports to examine how the time to polyp recurrence (731 patients experienced recurrence) is influenced by these characteristics as well as anthropometric features using Kaplan-Meier curves, Cox proportional hazards modeling, and random survival forest models. We found that the rate of recurrence differed significantly by polyp size, number, and location and patient smoking status. Additionally, right-sided colon polyps increased recurrence risk by 30% compared to left-sided polyps. History of tobacco use increased polyp recurrence risk by 20% compared to never-users. A random survival forest model showed an AUC of 0.65 and identified several other predictive variables, which can inform development of personalized polyp surveillance plans., Comment: Accepted in AMIA 2020 Informatics Summit