Search for: All records

Tools for computer-aided diagnosis based on deep learning have become increasingly important in the medical field. Such tools can be useful, but require effective communication of their decision-making process in order to safely and meaningfully guide clinical decisions. We present a user interface that incorporates the IAIA-BL model, which interpretably predicts both mass margin and malignancy for breast lesions. The user interface displays the most relevant aspects of the model’s explanation including the predicted margin value, the AI confidence in the prediction, and the two most highly activated prototypes for each case. In addition, this user interface includes full-field and cropped images of the region of interest, as well as a questionnaire suitable for a reader study. Our preliminary results indicate that the model increases the readers’ confidence and accuracy in their decisions on margin and malignancy. 

There is increasing interest in using deep learning and computer vision to help guide clinical decisions, such as whether to order a biopsy based on a mammogram. Existing networks are typically black box, unable to explain how they make their predictions. We present an interpretable deep-learning network which explains its predictions in terms of BI-RADS features mass shape and mass margin. Our model predicts mass margin and mass shape, then uses the logits from those interpretable models to predict malignancy, also using an interpretable model. The interpretable mass margin model explains its predictions using a prototypical parts model. The interpretable mass shape model predicts segmentations, fits an ellipse, then determines shape based on the goodness of fit and eccentricity of the fitted ellipse. While including mass shape logits in the malignancy prediction model did not improve performance, we present this technique as part of a framework for better clinician-AI communication. 

Abstract Achieving both high redox activity and rapid ion transport is a critical and pervasive challenge in electrochemical energy storage applications. This challenge is significantly magnified when using large‐sized charge carriers, such as the sustainable ammonium ion (NH₄⁺). A self‐assembled MXene/n‐type conjugated polyelectrolyte (CPE) superlattice‐like heterostructure that enables redox‐active, fast, and reversible ammonium storage is reported. The superlattice‐like structure persists as the CPE:MXene ratio increases, accompanied by a linear increase in the interlayer spacing of MXene flakes and a greater overlap of CPEs. Concurrently, the redox activity per unit of CPE unexpectedly intensifies, a phenomenon that can be explained by the enhanced de‐solvation of ammonium due to the increased volume of 3 Å‐sized pores, as indicated by molecular dynamic simulations. At the maximum CPE mass loading (MXene:CPE ratio = 2:1), the heterostructure demonstrates the strongest polymeric redox activity with a high ammonium storage capacity of 126.1 C g⁻¹and a superior rate capability at 10 A g⁻¹. This work unveils an effective strategy for designing tunable superlattice‐like heterostructures to enhance redox activity and achieve rapid charge transfer for ions beyond lithium.