</> Code with Papers by Syntology

Datasets › CHASE_DB1

CHASE_DB1

Introduced in An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation1 Jan 2012 archive 2025-07-28

CHASE_DB1 is a dataset for retinal vessel segmentation which contains 28 color retina images with the size of 999×960 pixels which are collected from both left and right eyes of 14 school children. Each image is annotated by two independent human experts.

Source: MixModule: Mixed CNN Kernel Module for Medical Image Segmentation Image Source: https://www.mdpi.com/2073-8994/9/11/276

HomepageBenchmarksPapers JSON

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Retinal Vessel Segmentation CHASE_DB1 FSG-Net AUC 0.9937 Full-scale Representation Guided Network for Retinal... zombasy/fsg-net-pytorch 16 Compare
Medical Image Segmentation CHASE_DB1 MERIT-GCASCADE DSC 0.8267 G-CASCADE: Efficient Cascaded Graph Convolutional... SLDGroup/G-CASCADE 3 Compare

Papers archive 2025-07-28

16 shown of 16 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 59. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation 1 1 2 Jun 2025 not harvested
Full-scale Representation Guided Network for Retinal Vessel Segmentation 1 1 31 Jan 2025 not harvested
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation 1 4 24 Oct 2023 ran 6 of 9 samples (3 unverified; 9 pointer-only for licence)
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks 0 1 26 Jul 2023 not harvested
Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation 1 1 5 Jul 2022 not harvested
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation 1 1 31 Mar 2021 not harvested
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels 1 1 5 Mar 2021 not harvested
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network 2 1 3 Jan 2021 not harvested
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation 4 1 7 Apr 2020 not harvested
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks 2 1 12 Dec 2019 not harvested
DUNet: A deformable network for retinal vessel segmentation 0 1 3 Nov 2018 not harvested
LadderNet: Multi-path networks based on U-Net for medical image segmentation 3 1 17 Oct 2018 not harvested
Deep Vessel Segmentation By Learning Graphical Connectivity 1 1 6 Jun 2018 not harvested
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation 12 1 20 Feb 2018 ran 5 of 8 samples (3 unverified; 5 pointer-only for licence)
Road Extraction by Deep Residual U-Net 13 1 29 Nov 2017 not harvested
U-Net: Convolutional Networks for Biomedical Image Segmentation 487 1 18 May 2015 ran 510 of 757 samples (247 unverified; 426 pointer-only for licence)

Dataset loaders archive 2025-07-28

open-mmlab/mmsegmentationopen-mmlab/mmsegmentationpytorch

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Medical Image SegmentationRetinal Vessel Segmentation

License archive 2025-07-28

Unknown

Modalities archive 2025-07-28

ImagesMedical

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • CHASE_DB1

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections