CatScreen: A Large MultiModal Benchmark Dataset for Cataract Screening

Mahapara Khurshid, Sonam Kumar, Dr Anusuya Bhattacharyya, Dhruve Kiyawat, Anshul Chauhan, Suklengmung Buragohain, Harsha Bhattacharjee, Limalemla Jamir, Vishali Gupta, Mona Duggal, Mayank Vatsa, Richa Singh

Abstract: Low-cost slit-lamp imaging holds significant potential for transforming eye care by facilitating affordable and scalable cataract diagnosis. However, the development of robust, generalizable AI-based cataract screening solutions is currently constrained by the limited availability of large-scale, richly annotated datasets. To address this critical gap, we introduce CatScreen, a comprehensive multimodal benchmark dataset specifically designed for cataract screening, comprising approximately 18,000 slit-lamp images collected from 2,251 subjects using a portable slit-lamp camera. CatScreen is structured into three subsets: (i) a clean set meticulously annotated using a structured multi-tier framework involving trained optometrists with final validation by an experienced ophthalmologist across clinically relevant dimensions, including image gradability, quality assessment, illumination type, diagnostic classification, cataract subtype, and severity grading according to established standards; (ii) a noisy-labeled set that simulates real-world annotation inaccuracies; and (iii) an unlabeled set intended to foster the development of self-supervised and semi-supervised learning approaches. Furthermore, CatScreen integrates extensive subject-level metadata encompassing demographics, lifestyle factors, and detailed clinical histories, and includes a subset with anatomical and pathological annotations to support multimodal modeling and anatomically grounded analysis. We present baseline experiments under independent, structured sequential, and multitask prediction settings in both unimodal and multimodal configurations. These results establish initial benchmarks for CatScreen and demonstrate the value of metadata for selected diagnostic tasks, while also highlighting open challenges, such as class imbalance and fine-grained subtype discrimination. CatScreen is intended as a benchmark resource for future research in cataract screening, robust learning, semi-supervised learning, and interpretability-oriented analysis. The database is available at: https://iab-rubric.org/resources/healthcare-datasets/catscreen.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission:

Submitting the camera ready version with the following updates:
- Addressed the feedback of the Action Editor
- Included the link of the dataset.

Assigned Action Editor: ~ERIC_EATON1

Submission Number: 5513