ML Research Wiki / Benchmarks / Medical Image Segmentation / MoNuSeg

MoNuSeg

Medical Image Segmentation Benchmark

Performance Over Time

📊 Showing 14 results | 📏 Metric: F1

Top Performing Models

Rank Model Paper F1 Date Code
1 Stardist Cell Detection with Star-convex Polygons 84.60 2018-06-09 📦 stardist/stardist 📦 uhlmanngroup/splinedist 📦 hthierno/pytorch-stardist
2 ReN-UNet Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion 84.12 2025-04-08 📦 saadwazir/ReN-UNet
3 LViT-L LViT: Language meets Vision Transformer in Medical Image Segmentation 81.01 2022-06-29 📦 huanglizi/lvit
4 MDM Masked Diffusion as Self-supervised Representation Learner 81.01 2023-08-10 📦 zx-pan/mdm
5 LViT-LW LViT: Language meets Vision Transformer in Medical Image Segmentation 80.66 2022-06-29 📦 huanglizi/lvit
6 UCTransNet LViT: Language meets Vision Transformer in Medical Image Segmentation 79.87 2022-06-29 📦 huanglizi/lvit
7 LoGo Medical Transformer: Gated Axial-Attention for Medical Image Segmentation 79.56 2021-02-21 📦 jeya-maria-jose/Medical-Transformer 📦 dani-capellan/ptb_lungregionextractor
8 MedT Medical Transformer: Gated Axial-Attention for Medical Image Segmentation 79.55 2021-02-21 📦 jeya-maria-jose/Medical-Transformer 📦 dani-capellan/ptb_lungregionextractor
9 GTUNet LViT: Language meets Vision Transformer in Medical Image Segmentation 79.26 2022-06-29 📦 huanglizi/lvit
10 UNet++ LViT: Language meets Vision Transformer in Medical Image Segmentation 77.01 2022-06-29 📦 huanglizi/lvit

All Papers (14)

stardist/stardist uhlmanngroup/splinedist hthierno/pytorch-stardist
saadwazir/ReN-UNet
huanglizi/lvit
zx-pan/mdm
huanglizi/lvit
huanglizi/lvit
jeya-maria-jose/Medical-Transformer dani-capellan/ptb_lungregionextractor
jeya-maria-jose/Medical-Transformer dani-capellan/ptb_lungregionextractor
huanglizi/lvit
huanglizi/lvit
jeya-maria-jose/Medical-Transformer dani-capellan/ptb_lungregionextractor
saadwazir/mcads-decoder
saadwazir/HistoSeg
gorkemcanates/dual-cross-attention