The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

Recent developments in deep learning have enabled data-driven algorithms that can reach
human-level performance and beyond. The development and deployment of medical image …

Colonoscopy, currently the most efficient and recognized colon polyp detection technology,
is necessary for early screening and prevention of colorectal cancer. However, due to the …

This paper presents a novel supervised convolutional neural network architecture,“DUCK-
Net”, capable of effectively learning and generalizing from small amounts of medical images …

Colonoscopy is widely recognised as the gold standard procedure for the early detection of
colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications …

Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated
polyp segmentation, a precancerous precursor, can minimize missed rates and timely …

Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …

Camouflaged object detection (COD) aims to detect out-of-attention regions in an image.
Current binary segmentation solutions fail to tackle COD easily, since COD is more …

Recently, deep convolutional neural networks (CNNs) have provided us an effective tool for
automated polyp segmentation in colonoscopy images. However, most CNN-based …

Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …