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 ...

Abstract: Medical image segmentation is essential for computer-assisted diagnosis since it allows exact extraction of anatomical structures from CT/MRI data. Traditional approaches struggle to handle ...

This study aims to investigate the application of visual information processing mechanisms in the segmentation of stem cell (SC) images. The cognitive principles underlying visual information ...

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.

A research team led by Prof. WANG Huanqin at the Institute of Intelligent Machines, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, recently proposed a semi-supervised ...

Laryngeal high-speed video (HSV) is a widely used technique for diagnosing laryngeal diseases. Among various analytical approaches, segmentation of glottis regions has proven effective in evaluating ...

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