# Image Classification with Consistent Supporting Evidence

Source: [Image Classification with Consistent Supporting Evidence](<https://www.sota2.com/research/paper/68fa6b331e209131cea2cce2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:43:15.362Z

Authors: Peiqi Wang, Ruizhi Liao, Daniel Moyer, Seth Berkowitz, Steven Horng, Polina Golland

Published: 2021-11-13T05:47:26.000Z

## Abstract

Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible and sufficient with respect to model predictions. We propose measures of model inconsistency and regularizers that promote more consistent evidence. We demonstrate our ideas in the context of edema severity grading from chest radiographs. We demonstrate empirically that consistent models provide competitive performance while supporting interpretation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.07048>)

[PDF](<https://arxiv.org/pdf/2111.07048.pdf>)