# Image Classification with Consistent Supporting Evidence

arXiv: https://arxiv.org/abs/2111.07048 · HTML: https://mlsanity.com/paper/2111.07048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [A Semantic Loss Function for Deep Learning with Symbolic Knowledge](https://mlsanity.com/paper/1711.11157/) (2017)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [A Logic-Driven Framework for Consistency of Neural Models](https://mlsanity.com/paper/1909.00126/) (2019)
- [Interpretability Beyond Classification Output: Semantic Bottleneck Networks](https://mlsanity.com/paper/1907.10882/) (2019)
- [Rationale-Augmented Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1605.04469/) (2016)
- [Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment](https://mlsanity.com/paper/2008.09884/) (2020)
- [TED: Teaching AI to Explain its Decisions](https://mlsanity.com/paper/1811.04896/) (2018)
- [Semantic bottleneck for computer vision tasks](https://mlsanity.com/paper/1811.02234/) (2018)
- [Deep Learning to Quantify Pulmonary Edema in Chest Radiographs](https://mlsanity.com/paper/2008.05975/) (2020)
- [Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction](https://mlsanity.com/paper/2103.02768/) (2021)
- [Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images](https://mlsanity.com/paper/1902.10785/) (2019)
- [Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning](https://mlsanity.com/paper/1906.02299/) (2019)

## Built on by (most cited first, 3 of 3)

- [Consistency-preserving Visual Question Answering in Medical Imaging](https://mlsanity.com/paper/2206.13296/) (2022)
- [Efficient HLA imputation from sequential SNPs data by Transformer](https://mlsanity.com/paper/2211.06430/) (2022)
- [Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning](https://mlsanity.com/paper/2603.08921/) (2026)

## Abstract (arXiv)

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.