463 citations · 5.1k across the 190 of their papers we have counts for
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Explainable multiple abnormality classification of chest CT volumes
Rachel Lea Draelos, Lawrence Carin
Understanding model predictions is critical in healthcare, to facilitate rapid verification of model correctness and to guard against use of models that exploit confounding variabl…
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks
Rachel Lea Draelos, Lawrence Carin
Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations. We illustrate a previously unrecognized limitati…
Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations
John McManigle, Raquel Bartz, Lawrence Carin
Over the last decade, convolutional neural networks (CNNs) have emerged as the leading algorithms in image classification and segmentation. Recent publication of large medical imag…
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
Rachel Lea Draelos, David Dov, Maciej A. Mazurowski +4
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data s…
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Yantao Lu, Yunhan Jia, Jianyu Wang +4
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other m…