# Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks

arXiv: https://arxiv.org/abs/1904.03127 · HTML: https://mlsanity.com/paper/1904.03127/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis](https://mlsanity.com/paper/2203.17085/) (2022)
- [BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia](https://mlsanity.com/paper/2510.03004/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing. A Deep Neural Network (DNN), inspired by Bag-of-Features models is equipped with a Multiple Instance Learning (MIL) branch and trained with weak supervision for WSI classification. MIL avoids label ambiguity and enhances our model's expressive power without guiding its attention. We utilize a fine-grained logit heatmap of the models activations to interpret its decision-making process. The proposed method is quantitatively and qualitatively evaluated on two challenging histology datasets, outperforming a variety of baselines. In addition, two expert pathologists were consulted regarding the interpretability provided by our method and acknowledged its potential for integration into several clinical applications.