Abstract: The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, ...
Abstract: Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled data to ...
Qwen-Image doesn't just create or edit, it understands. It supports a suite of image understanding tasks, including object detection, semantic segmentation, depth and edge (Canny) estimation, novel ...
Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
Segmentations of retinal optical coherence tomography (OCT) images provide valuable information about each specific retinal layer. However, processing images from degenerative retina remains ...
In recent years, semi-supervised methods have been rapidly developed for three-dimensional (3D) medical image analysis. However, previous semi-supervised methods for three-dimensional medical images ...
Visual example of our conformal margin: we build a morphological margin (via dilation) that covers all missed pixels (false negatives). Dataset: WBC In the following image, we have a ground truth mask ...
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