Image segmentation - general superpixel segmentation & center detection & region growing

A paper list for medical anomaly detection. ℱℯℯ𝓁 𝒻𝓇ℯℯ to contribute!

🤖 PyTorch toolkit for biomedical imaging ❤️

Repository for the paper "Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations" (2024).

A Rat fMRI multi-center study

[CVPRW 2025] Official code of "IAUNet: Instance-Aware U-Net"

🧑🏻‍⚕️ 医学人工智能入门指南 Medical-AI-Guide

[JBHI 2025] BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models

Bone suppression in chest X-rays: A deep survey. ℱℯℯ𝓁 𝒻𝓇ℯℯ to contribute!

Fully automated 3D perivascular space (PVS) segmentation tool for T1w and T2w brain MRI.

EndoL2H: Deep Super-Resolution for Capsule Endoscopy

Histology Image Viewer and Converter

🎯 Top 204 solution for Elucidata AI Challenge 2025 – Predicting spatial cell-type composition from histology images using CNNs with EfficientNet & ResNet backbones, multi-scale patching, and coordinate-aware ensemble modeling.

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

This repository contains a Python application using OpenCV for detecting mitosis in images. Aimed at researchers and biologists, it provides tools to automate the counting and identification of mitotic figures in microscopic images, supporting studies in cellular biology and medical diagnostics.

U-Net & Attention U-Net for Cell Counting in Microscopy Images | PyTorch | Kaggle Competition | MAE 2.186

Semi-automatic deep learning pipeline for nuclei detection, muscle fibre segmentation and quantitative biological index computation in fluorescence microscopy images (FSHD context).

Multi-scale nuclei segmentation model for breast cancer histopathology using contextual patches, VGG16-based feature extraction, channel attention, and a U-Net decoder. Official Python implementation of the ISBI 2025 paper “M3-Net” with training, evaluation, and visualization workflows.

Biomedical image classification using deep learning, comparing a baseline CNN with a fine-tuned ResNet50 model for pneumonia detection, including trained model weights and reproducible notebooks.

This work was done just to present the idea that could be beneficial if used with computer science for medical imaging