Global explainability of a deep abstaining classifier

no code implementations • 1 Apr 2025 • Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Benjamin H. McMahon, Trilce Estrada, Kumkum Ganguly, Adam Spannaus, John P. Gounley, Xiao-Cheng Wu, Eric B. Durbin, Heidi A. Hanson, Tanmoy Bhattacharya

We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based deep abstaining classifier (DAC), for automated annotation of cancer pathology reports from NCI-SEER registries.

Dimensionality Reduction

Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping

no code implementations • 5 Jan 2021 • Mohammed Alawad, Shang Gao, Mayanka Chandra Shekar, S. M. Shamimul Hasan, J. Blair Christian, Xiao-Cheng Wu, Eric B. Durbin, Jennifer Doherty, Antoinette Stroup, Linda Coyle, Lynne Penberthy, Georgia Tourassi

Word embeddings that effectively capture the meaning and context of the word that they represent can significantly improve the performance of downstream DL models for various NLP tasks.

Word Embeddings

Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports

no code implementations • 10 Sep 2020 • Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Kumkum Ganguly, Gopinath Chennupati, Sunil Thulasidasan, Nicolas W. Hengartner, Brent J. Mumphrey, Eric B. Durbin, Jennifer A. Doherty, Mireille Lemieux, Noah Schaefferkoetter, Georgia Tourassi, Linda Coyle, Lynne Penberthy, Benjamin H. McMahon, Tanmoy Bhattacharya

We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest.