[IEEE TMI Best Paper Award] Official Implementation for UNet++
High-school research portfolio: 2 peer-reviewed publications (IEEE, IJHSR) + 3 active projects spanning quantum-inspired ML, computational biology, biomedical imaging, wildfire RL, and science-education research. Stanford Medicine, UCLA COSMOS & UCSB SRA affiliated.
Image segmentation - general superpixel segmentation & center detection & region growing
A paper list for medical anomaly detection. ℱℯℯ𝓁 𝒻𝓇ℯℯ to contribute!
Repository for the paper "Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations" (2024).
🤖 PyTorch toolkit for biomedical imaging ❤️
🩺 医学人工智能科研指南 · Medical-AI-Guide
[CVPRW 2025] Official code for "IAUNet: Instance-Aware U-Net"
A Rat fMRI multi-center study
[JBHI 2025] BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
Fully automated 3D perivascular space (PVS) segmentation tool for T1w and T2w brain MRI.
Bone suppression in chest X-rays: A deep survey. ℱℯℯ𝓁 𝒻𝓇ℯℯ to contribute!
This repository provides DINOSim, a method that leverages the DINOv2 foundation model for zero-shot object detection and segmentation in bioimage analysis. DINOSim uses pretrained DINOv2 embeddings to compare patch similarities, allowing it to detect and segment unseen objects in complex datasets with minimal annotations.
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
2D UNETR implementation in Tensorflow-keras, for EM image segmentation.
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.
A 3D Slicer extension for interactive 3D visualization and quantitative colocalization analysis of multi-channel confocal z-stack images.
Generate Synthetic Shapes in 3D for Biomedical Image Augmentation and Synthesis.
Supplementary repository for the paper “Detection and Classification of Breast Lesions in Biomedical Images Using Explainable Machine Learning Techniques” — Read the article .
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