Yufan He

A 3D foundation model that supports a full segmentation workflow. [code] [paper].

A medical VLM that utilizes domain knowledge via expert models. [code] [paper].

Upgraded SwinUNETR with convolutions. [code] [paper].

A hypernet for one-shot neural archtecture search for 3D medical image segmentation. [paper].

Efficient hyperparameter search. [code] [paper].

A differentiable neural archtecture search method for 3D medical image segmentation. It contains a new search space and a novel multi-path relaxation scheme for differentiable search. [code] [paper].

A state-of-the-art unsupervised 3D medical image registration package using swin transformer. [code] [paper].

An ealier conference version "ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration", MIDL 2021, is available [code] [paper].

A test-time self domain adaptation method is proposed. It uses auto-encoders' reconstruction loss as a source-target alignment measurement. A set of adaptors are trained and used for domain adaptation in test-time. [code] [paper].

The journal version "Autoencoder based self-supervised test-time adaptation for medical image analysis", Medical Image Analysis 2021, is available [paper].

A novel fully convolutional boundary regression method is proposed for efficient, end-to-end, and topology guaranteed surface segmentation. [code] [paper].

The journal version "Structured layer surface segmentation for retina OCT using fully convolutional regression networks", Medical Image Analysis 2020, is also available [paper].