Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

This paper proposes a novel methodology for automatic detection and localization of
gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed …

In this paper, we propose a novel Fully Convolutional Neural Network (FCN) architecture
aiming to aid the detection of abnormalities, such as polyps, ulcers and blood, in …

Obtaining accurate segmentation of the prostate and nearby organs at risk (eg, bladder and
rectum) in CT images is critical for radiotherapy of prostate cancer. Currently, the leading …

Sufficient data with complete annotation is essential for training deep models to perform
automatic and accurate segmentation of CT male pelvic organs, especially when such data …

Goal: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise
labeled data to train various supervised machine learning models. However, it is a tedious …

Several studies have addressed the problem of abnormality detection in medical images
using computer-based systems. The impact of such systems in clinical practice and in the …

The detection of abnormalities in endoscopic video frames can contribute in the early and
more accurate detection of pathologic conditions. In this paper we present a novel …

This doctoral dissertation explores intelligent systems and services for image and video
analysis. In view of scientific challenges for developing innovative solutions with a broad …