IEEE

Text-Driven Medical Image Segmentation With Text-Free Inference via Knowledge Distillation

Abstract: Medical image segmentation plays a pivotal role in ensuring accurate diagnosis. Traditional methods are predominantly monomodal, relying solely on image data. These image-only methods ...
GitHub

Slim UNETR: An Efficient, High-Quality 3D Segmentation for Medical Image Analysis with Constrained Computational Resources

🎉 This work is published in IEEE Transactions on Medical Imaging The sub-regions considered for evaluation in BraTS 21 challenge are the "enhancing tumor" (ET), the "tumor core" (TC), and the "whole ...
IEEE

Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels

Abstract: The success of deep learning in 3D medical image segmentation hinges on training with a large dataset of fully annotated 3D volumes, which are difficult and time-consuming to acquire.
GitHub

VeloxSeg: Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

Overview of VeloxSeg. VeloxSeg employs an encoder-decoder architecture with Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC) on the left, using 1x1 convolution as ...