Fenghe Tang

U-Bench. U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking

CMUNeXt. [ISBI 2024 Oral] Official Pytorch Code base for "CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion"

Medical-Image-Segmentation-Benchmarks. A Pytorch implement of medical image segmentation U-shape architecture benchmarks

CMU-Net. [ISBI 2023] Official Pytorch implementation of "CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network"

Mobile-U-ViT. [ACM MM 2025] Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation

LLM4Seg. [MICCAI 2025] Official code for "Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster"

Multi-Level-Global-Context-Cross-Consistency. Official Pytorch Code base for "Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model"

MambaMIM. [MedIA 2025] MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation

Hi-End-MAE. [MedIA 2026] Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

MobileUtr. Official Pytorch Code base for "MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation"

HySparK. [MICCAI 2024] HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training

FengheTan9.github.io. personal info

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