In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.
It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation.
Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a significant domain discrepancy between different cameras.
Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.
In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images.
More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images.
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
Specifically, our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch.
Our contribution consists of the proposal of a significant task worth investigating and a naive baseline of solving it.
Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis.
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
Ranked #1 on Nuclear Segmentation on Cell17
In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.