Retinal OCT Disease Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (12 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.

• Deep Residual Learning for Image Recognition
  10 Dec 2015 484 repositories listed Syntology ran 230 of 377 samples · 147 unverified · 187 pointer-only (licence)
  Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
• MobileNetV2: Inverted Residuals and Linear Bottlenecks
  13 Jan 2018 159 repositories listed Syntology ran 85 of 111 samples · 26 unverified · 64 pointer-only (licence)
  In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
• Rethinking the Inception Architecture for Computer Vision
  2 Dec 2015 113 repositories listed Syntology ran 5 of 26 samples · 21 unverified · 4 pointer-only (licence)
  Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.
• Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
  13 Oct 2019 2 repositories listed
  Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task.
• UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
  28 Feb 2025 1 repository listed
  Anomaly detection (AD) is a crucial visual task aimed at recognizing abnormal pattern within samples.
• ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification
  28 Jan 2025 1 repository listed
  Optical Coherence Tomography (OCT) is a non-invasive imaging modality essential for diagnosing various eye diseases.
• TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
  30 Jun 2022 1 repository listed
  Recent contrastive learning methods achieved state-of-the-art in low label regimes.
• Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
  23 Oct 2020 1 repository listed
  Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome…
• Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
  16 May 2020 1 repository listed
  Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases.
• Retinal OCT disease classification with variational autoencoder regularization
  23 Mar 2019 1 repository listed
  A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images.

Syntology lines on 3 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.