Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Deep-learning methods for auto-segmenting brain images either segment one slice of the
image (2D), five consecutive slices of the image (2.5 D), or an entire volume of the image …

Efficient polyp segmentation in healthcare plays a critical role in enabling early diagnosis of
colorectal cancer. However, the segmentation of polyps presents numerous challenges …

Capsule network is a recent new deep network architecture that has been applied
successfully for medical image segmentation tasks. This work extends capsule networks for …

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a
key predictor of embryo viability. However, observing cell divisions in Time-Lapse …

Abstract 3D image reconstruction from a limited number of 2D images has been a long-
standing challenge in computer vision and image analysis. While deep learning-based …

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining
where a radiologist looks, typically through tasks such as detection, segmentation, or …

Convolutional Neural Networks (CNNs) have achieved promising results in medical image
segmentation. However, CNNs require lots of training data and are incapable of handling …

Recently, deep learning has known a remarkable mutation in computer vision, which has
been optimally exploited to solve various complex tasks and improve their results in the …

In this paper, we present Combined-CapsNet (C-CapsNet), a novel approach aimed at
enhancing the performance and explainability of Capsule Neural Networks (CapsNets) in …