ArticleScientific reports2023
Semi-supervised segmentation of retinoblastoma tumors in fundus images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Review
- Investigating the Impact of Semi-Supervised Learning Methods to Improve the Quality of Diagnosis of Retinal Diseases from OCT Images.Diagnostics (Basel, Switzerland) · 2026Article
- Toward automated assessment of conjunctival hyperemia: A semisupervised artificial intelligence approach.Annals of the New York Academy of Sciences · 2025Article
- An ultra-wide-field fundus image dataset for intelligent diagnosis of intraocular tumors.Scientific data · 2025Article
- RB-Care: An Artificial Intelligence System for Automatic Quantitative Assessment and Surveillance of Retinoblastoma.Translational vision science & technology · 2025Article
- Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2025Article
- Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort.Cancers · 2024Article
- FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts.Bioengineering (Basel, Switzerland) · 2024Article
- A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.Translational vision science & technology · 2024Article
- Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinoblastoma is a rare form of cancer that predominantly affects young children as the primary intraocular malignancy. Studies conducted in developed and some developing countries have revealed that early detection can successfully cure over 90% of children with retinoblastoma. An unusual white reflection in the pupil is the most common presenting symptom. Depending on the tumor size, shape, and location, medical experts may opt for different approaches and treatments, with the results varying significantly due to the high reliance on prior knowledge and experience. This study aims to present a model based on semi-supervised machine learning that will yield segmentation results comparable to those achieved by medical experts. First, the Gaussian mixture model is utilized to detect abnormalities in approximately 4200 fundus images. Due to the high computational cost of this process, the results of this approach are then used to train a cost-effective model for the same purpose. The proposed model demonstrated promising results in extracting highly detailed boundaries in fundus images. Using the Sørensen-Dice coefficient as the comparison metric for segmentation tasks, an average accuracy of 93% on evaluation data was achieved.
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Registered trials
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