4 papers
Global explainability of a deep abstaining classifier
Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Benjamin H. McMahon +8
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…
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Mayanka Chandrashekar, Ian Goethert, Md Inzamam Ul Haque +18
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic f…
An Effective Baseline for Robustness to Distributional Shift
Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel +3
Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe deployment of deep le…
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Kumkum Ganguly +13
Safe deployment of deep learning systems in critical real world applications requires models to make very few mistakes, and only under predictable circumstances. In this work, we a…