[ICCV2023] XNet: Wavelet-Based Low and High Frequency Merging Networks for Semi- and Supervised Semantic Segmentation of Biomedical Images

[BIBM 2024] XNet v2: Fewer Limitations, Better Results and Greater Universality

[TMI 2024.10] GobletNet: Wavelet-Based High Frequency Fusion Network for Semantic Segmentation of Electron Microscopy Images

[CVPR 2025] nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark

Generate Synthetic Shapes in 3D for Biomedical Image Augmentation and Synthesis.

How To Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-Angle Maximum Intensity Projections and Diffusion Models

Biomedical Image Segmentation using Unet with PyTorch

[Bioinformatics 2025.01] A Semi-Supervised Fracture-Attention Model for Segmenting Tubular Objects with Improved Topological Connectivity

Segmentation of Biomedical Images is based on U-Net. This U-Net implementation using Keras and TensorFlow has varying depth that can be specified by model input.

The repo of the ANN's class final project in NCU (Toruń, Poland). It is an implementation of the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation".