Diabetic Retinopathy Detection

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (53 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
  21 Jan 2019 3 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed.
• Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation
  31 Mar 2017 2 repositories listed
  We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection.
• Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
  25 Mar 2025 1 repository listed
  Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion.
• Conformal Risk Control for Ordinal Classification
  1 May 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems.
• Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network
  5 Jan 2023 1 repository listed
  The image of retinal fundus is the main evaluating strategy for the glaucoma and diabetic retinopathy detection.
• UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
  6 Nov 2022 1 repository listed
  Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy.
• Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images
  9 Jul 2022 1 repository listed
  Blood Vessels and Exudates.
• The Manifold Hypothesis for Gradient-Based Explanations
  15 Jun 2022 1 repository listed Syntology ran 1 of 11 samples · 10 unverified
  We propose a criterion: the feature attributions need to be aligned with the tangent space of the data manifold.
• Explainable Diabetic Retinopathy Detection and Retinal Image Generation
  1 Jul 2021 1 repository listed
  Though deep learning has shown successful performance in classifying the label and severity stage of certain diseases, most of them give few explanations on how to make predictions.
• Curvature-based Feature Selection with Application in Classifying Electronic Health Records
  10 Jan 2021 1 repository listed
  Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML)…
• A Unified Technique for Entropy Enhancement Based Diabetic Retinopathy Detection Using Hybrid Neural Network
  1 Jul 2020 1 repository listed
  In this paper, a unified technique for entropy enhancement-based diabetic retinopathy detection using a hybrid neural network is proposed for diagnosing diabetic retinopathy.
• 3D Self-Supervised Methods for Medical Imaging
  6 Jun 2020 1 repository listed Syntology ran 0 of 16 samples · 16 unverified · 16 pointer-only (licence)
  Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields.
• Deep Learning Approach to Diabetic Retinopathy Detection
  3 Mar 2020 1 repository listed
  In this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus.
• O-MedAL: Online Active Deep Learning for Medical Image Analysis
  28 Aug 2019 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  Our online method enhances performance of its underlying baseline deep network.
• Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
  17 May 2019 1 repository listed
  Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems.
• Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
  12 Mar 2018 1 repository listed
  We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22).

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.