# ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning

arXiv: https://arxiv.org/abs/2403.05280 · HTML: https://mlsanity.com/paper/2403.05280/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification](https://mlsanity.com/paper/1806.00712/) (2018)
- [Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning](https://mlsanity.com/paper/2107.05605/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond](https://mlsanity.com/paper/2606.16153/) (2026)

## Abstract (arXiv)

With the ongoing development of deep learning, an increasing number of AI models have surpassed the performance levels of human clinical practitioners. However, the prevalence of AI diagnostic products in actual clinical practice remains significantly lower than desired. One crucial reason for this gap is the so-called `black box' nature of AI models. Clinicians' distrust of black box models has directly hindered the clinical deployment of AI products. To address this challenge, we propose ContrastDiagnosis, a straightforward yet effective interpretable diagnosis framework. This framework is designed to introduce inherent transparency and provide extensive post-hoc explainability for deep learning model, making them more suitable for clinical medical diagnosis. ContrastDiagnosis incorporates a contrastive learning mechanism to provide a case-based reasoning diagnostic rationale, enhancing the model's transparency and also offers post-hoc interpretability by highlighting similar areas. High diagnostic accuracy was achieved with AUC of 0.977 while maintain a high transparency and explainability.