{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:58:39Z","timestamp":1772103519430,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Funds","award":["UID\/50014\/2025"],"award-info":[{"award-number":["UID\/50014\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Eye diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration, drive the growing need for reliable and scalable analyses of fundus and optical coherence tomography (OCT) images. Deep learning performs strongly in ocular structure segmentation. However, it typically relies on dense pixel-wise annotations, which are costly and difficult to obtain at scale. Weakly supervised learning (WSL) can reduce this burden by leveraging coarse labels, limited strong annotations, and unlabeled data. This systematic umbrella review synthesizes survey and review articles on weakly supervised deep learning for image segmentation, with a focus on ocular imaging (fundus and OCT\/OCTA). After analyzing twenty-one secondary studies, the main finding reveals an \u201cempty intersection\u201d: WSL-focused segmentation surveys are often modality-agnostic. Conversely, ocular reviews are predominantly fully supervised and seldom offer quantitative evidence on annotation-effort savings or direct comparisons between weak and fully supervised methods on identical datasets. Across the included reviews, label-efficient strategies cluster around CAM\/MIL formulations, sparse supervision (points\/scribbles\/boxes), pseudo-labelling\/self-training, and semi-\/self-supervised learning, implemented mainly with U-Net\/DeepLab families and increasingly Transformer or hybrid backbones. These results provide a structured map of available WSL mechanisms and, critically, identify reproducible reporting gaps that currently prevent fair benchmarking in ocular segmentation. Therefore, this review supports the development of ocular-specific benchmarks and minimum reporting practices that link segmentation performance to annotation effort.<\/jats:p>","DOI":"10.3390\/app16052241","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T09:31:32Z","timestamp":1772098292000},"page":"2241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Weakly Supervised Deep Learning for Ocular Image Segmentation: A Systematic Review of Fundus and OCT Methods"],"prefix":"10.3390","volume":"16","author":[{"given":"Pedro","family":"Penedo","sequence":"first","affiliation":[{"name":"Department of Sciences and Technologies, Universidade Aberta (UAb), 1269-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6926-7848","authenticated-orcid":false,"given":"Jorge","family":"Machado","sequence":"additional","affiliation":[{"name":"School Sciences and Technologies, Universidade de Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9058-5836","authenticated-orcid":false,"given":"Rita","family":"Anjos","sequence":"additional","affiliation":[{"name":"Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, 1150-199 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3495-4649","authenticated-orcid":false,"given":"Ana","family":"Marta","sequence":"additional","affiliation":[{"name":"Unidade Local de Sa\u00fade de Santo Ant\u00f3nio, Department of Ophthalmology, 4099-001 Porto, Portugal"},{"name":"Unit for Multidisciplinary Research in Biomedicine, Instituto de Ci\u00eancias Biom\u00e9dicas Abel Salazar (ICBAS), Universidade do Porto, 4050-346 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0423-2514","authenticated-orcid":false,"given":"Arist\u00f3fanes Corr\u00eaa","family":"Silva","sequence":"additional","affiliation":[{"name":"Applied Computer Group Department of Electrical Engineering, Federal University of Maranh\u00e3o, S\u00e3o Lu\u00eds 65085-580, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Cunha","sequence":"additional","affiliation":[{"name":"School Sciences and Technologies, Universidade de Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"},{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2147\/OPTH.S348479","article-title":"Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation\u2014A Review","volume":"2022","author":"Alawad","year":"2022","journal-title":"Clin. 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