# ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis

arXiv: https://arxiv.org/abs/2008.05731 · HTML: https://mlsanity.com/paper/2008.05731/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
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- [Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images](https://mlsanity.com/paper/2310.01912/) (2023)
- [A clinically motivated self-supervised approach for content-based image retrieval of CT liver images](https://mlsanity.com/paper/2207.04812/) (2022)
- [Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy](https://mlsanity.com/paper/2211.11509/) (2022)
- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2401.05137/) (2024)
- [Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images](https://mlsanity.com/paper/2304.11168/) (2023)
- [A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy](https://mlsanity.com/paper/2309.00911/) (2023)
- [An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition](https://mlsanity.com/paper/2309.00903/) (2023)
- [Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction](https://mlsanity.com/paper/2401.03615/) (2024)
- [Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis](https://mlsanity.com/paper/2201.09421/) (2022)
- [eXplainable Artificial Intelligence on Medical Images: A Survey](https://mlsanity.com/paper/2305.07511/) (2023)
- [Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model](https://mlsanity.com/paper/2401.13990/) (2024)
- [The Explanation Necessity for Healthcare AI](https://mlsanity.com/paper/2406.00216/) (2024)

## Abstract (arXiv)

In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support. However, the "black-box" nature of successful AI algorithms still holds back their wide-spread deployment. In this paper, we describe an eXplanatory Artificial Intelligence (XAI) that reaches the same level of performance as black-box AI, for the task of classifying Diabetic Retinopathy (DR) severity using Color Fundus Photography (CFP). This algorithm, called ExplAIn, learns to segment and categorize lesions in images; the final image-level classification directly derives from these multivariate lesion segmentations. The novelty of this explanatory framework is that it is trained from end to end, with image supervision only, just like black-box AI algorithms: the concepts of lesions and lesion categories emerge by themselves. For improved lesion localization, foreground/background separation is trained through self-supervision, in such a way that occluding foreground pixels transforms the input image into a healthy-looking image. The advantage of such an architecture is that automatic diagnoses can be explained simply by an image and/or a few sentences. ExplAIn is evaluated at the image level and at the pixel level on various CFP image datasets. We expect this new framework, which jointly offers high classification performance and explainability, to facilitate AI deployment.