Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and
actionable insights by seamlessly integrating disparate biomedical data from multiple …

In pathological image analysis, determination of gland morphology in histology images of
the colon is essential to determine the grade of colon cancer. However, manual …

Salient object detection (SOD) is an important task in computer vision that aims to identify
visually conspicuous regions in images. RGB-Thermal SOD combines two spectra to …

Supervised machine learning requires training on the dataset with annotation. However, fine-
grained annotation is very expensive to acquire. In the medical image analysis domain, the …

Purpose A clinically compatible computerized segmentation model is presented here that
aspires to supply clinical gland informative details by seizing every small and intricate …

A network based on complementary consistency training, CC-Net, has been proposed for
semi-supervised left atrium image segmentation. CC-Net efficiently utilizes unlabeled data …

Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level
annotation in medical image segmentation tasks. Consistency learning, serving as a …

In pathological image analysis, determination of gland morphology in histology images of
the colon is essential to determine the grade of colon cancer. However, manual …

Recently, significant progress has been made in consistency regularization-based semi-
supervised medical image segmentation. Typically, a consistency loss is applied to enforce …