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Found 4 papers, 4 papers with code
Explainable multiple abnormality classification of chest CT volumes
We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images, in which a model must indicate the regions used to predict each abnormality.
Playing Codenames with Language Graphs and Word Embeddings
Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored.
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks
Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations.
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
This model reached a classification performance of AUROC greater than 0. 90 for 18 abnormalities, with an average AUROC of 0. 773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data.
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