Abstract: Recently, single-source domain generalization (SDG) has gained popularity in medical image segmentation. As a prominent technique, adversarial image augmentation technique can generate ...

Abstract: Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often ...

This project demonstrates instance segmentation using Mask R-CNN with the OpenCV DNN module. The model is pre-trained on the COCO dataset and can detect and segment multiple object classes in images.

Medical image segmentation is at the heart of modern healthcare AI, enabling crucial tasks such as disease detection, progression monitoring, and personalized treatment planning. In disciplines like ...

To address the trade-off between segmentation performance and model lightweighting in computer-aided skin lesion segmentation, this paper proposes a lightweight network architecture, Multi-Conv ...

Code repository for training a brain tumour U-Net 3D image segmentation model using the 'Task1 Brain Tumour' medical segmentation decathlon challenge dataset.