Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early
detection and diagnosis, comprehensive assessment of treatment response, and precise …

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal
(ie, healthy) images to detect any abnormal (ie, unhealthy) samples that do not conform to …

Colorectal cancer is a major health problem, where advances towards computer-aided
diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement …

There are two challenges associated with the interpretability of deep learning models in
medical image analysis applications that need to be addressed: confidence calibration and …

The training of deep learning models generally requires a large amount of annotated data
for effective convergence and generalisation. However, obtaining high-quality annotations is …

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy)
images only, but during testing, they are able to classify normal and abnormal (or disease) …

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a
detector using a training set that contains only normal images. UAD approaches can be …

Current polyp detection methods from colonoscopy videos use exclusively normal (ie,
healthy) training images, which i) ignore the importance of temporal information in …

At present, the incidence rate of colorectal cancer (CRC) is increasing year by year. It has
always affected people's physical and mental health and quality of life. How to improve the …