# This actually looks like that: Proto-BagNets for local and global interpretability-by-design

arXiv: https://arxiv.org/abs/2406.15168 · HTML: https://mlsanity.com/paper/2406.15168/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet](https://mlsanity.com/paper/1904.00760/) (2019)
- [XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations](https://mlsanity.com/paper/2103.10663/) (2021)
- [IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography](https://mlsanity.com/paper/2103.12308/) (2021)
- [This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation](https://mlsanity.com/paper/2108.12204/) (2021)
- [Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks](https://mlsanity.com/paper/2212.05946/) (2022)
- [On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis](https://mlsanity.com/paper/2310.06966/) (2023)
- [Sanity checks for patch visualisation in prototype-based image classification](https://mlsanity.com/paper/2311.16120/) (2023)

## Built on by (most cited first, 7 of 7)

- [CountXplain: Interpretable Cell Counting with Prototype-Based Density Map Estimation](https://mlsanity.com/paper/2511.19686/) (2025)
- [Soft-CAM: Making black box models self-explainable for medical image analysis](https://mlsanity.com/paper/2505.17748/) (2025)
- [PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis](https://mlsanity.com/paper/2506.10669/) (2025)
- [ProtoMask: Segmentation-Guided Prototype Learning](https://mlsanity.com/paper/2510.00683/) (2025)
- [Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction](https://mlsanity.com/paper/2510.06113/) (2025)
- [TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs](https://mlsanity.com/paper/2603.26885/) (2026)
- [Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening](https://mlsanity.com/paper/2608.18759/) (2026)

## Abstract (arXiv)

Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on post-hoc methods that do not faithfully reflect the model's behavior. As a remedy, prototype-based networks have been proposed, but their interpretability is limited as they have been shown to provide coarse, unreliable, and imprecise explanations. In this work, we introduce Proto-BagNets, an interpretable-by-design prototype-based model that combines the advantages of bag-of-local feature models and prototype learning to provide meaningful, coherent, and relevant prototypical parts needed for accurate and interpretable image classification tasks. We evaluated the Proto-BagNet for drusen detection on publicly available retinal OCT data. The Proto-BagNet performed comparably to the state-of-the-art interpretable and non-interpretable models while providing faithful, accurate, and clinically meaningful local and global explanations. The code is available at https://github.com/kdjoumessi/Proto-BagNets.