Abstract
Two-dimensional echocardiography is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Accurate segmentation of the left ventricle in echocardiography is vital for ensuring the accuracy of subsequent diagnosis. Currently, numerous efforts have been made to automatize this task and various public datasets have been released in recent decades to further develop present research. However, medical datasets acquired at different institutions have inherent bias caused by various confounding factors, such as operation policies, machine protocols, treatment preference, etc. As a result, models trained on one dataset, regardless of volume, cannot be confidently utilized for the others. In this study, we investigated model robustness to dataset bias using two publicly available echocardiographic datasets. This work validates the efficacy of a supervised deep learning model for left ventricle segmentation and ejection fraction prediction, outside the dataset on which it was trained. The exposure of this model to unseen, but related samples without additional training maintained a good performance. However, a performance decrease from the original results can be observed, while the impact of quality is also noteworthy with lower quality data leading to decreased performance.

Abstract
The gastrointestinal tract is responsible for the entire digestive process. Several diseases, including colorectal cancer, can affect this pathway. Among the deadliest cancers, colorectal cancer is the second most common. It arises from benign tumors in the colon, rectum, and anus. These benign tumors, known as colorectal polyps, can be diagnosed and removed during colonoscopy. Early detection is essential to reduce the risk of cancer. However, approximately 28% of polyps are lost during this examination, mainly because of limitations in diagnostic techniques and image analysis methods. In recent years, computer-aided detection techniques for these lesions have been developed to improve detection quality during periodic examinations. We proposed an automatic method for polyp detection using colonoscopy images. This study presents a two-stage polyp detection method for colonoscopy images using transformers. In the first stage, a saliency map extraction model is supported by the extracted depth maps to identify possible polyp areas. The second stage of the method consists of detecting polyps in the extracted images resulting from the first stage, combined with the green and blue channels. Several experiments were performed using four public colonoscopy datasets. The best results obtained for the polyp detection task were satisfactory, reaching 91% Average Precision in the CVC-ClinicDB dataset, 92% Average Precision in the Kvasir-SEG dataset, and 84% Average Precision in the CVC-ColonDB dataset. This study demonstrates that polyp detection in colonoscopy images can be efficiently performed using a combination of depth maps, salient object-extracted maps, and transformers.

Abstract
In this paper, we present a numerical comparison of how well segmentation algorithms approximate the manual segmentation of gastroenterologists for a set of endoscopic images. Different areas in these images demand different levels of analysis by a clinician and some provide critical information about the patient. Our objective is thus to segment endoscopic images so that the results mimic as closely as possible the areas that were considered relevant by doctors. We focus on a detailed quantitative comparison of two popular segmentation algorithms, mean shift and normalized cuts, when applied to in-body images, most specifically for vital-stained magnification endoscopy. Segmentation results are compared with the manual annotations of the same images performed by two specialist clinicians. Results show that if we simply consider the most relevant segmented patch, normalized cuts performs better. However, if we allow the annotated area to be represented by multiple patches, mean shift is clearly a better choice, although automatic ways to determine its kernel's bandwidth are highly desirable.

Abstract
Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. In this paper, we propose a new approach for arrhythmia classification based on a combination of morphological and dynamic features. Wavelet Transform (WT) and Independent Component Analysis (ICA) are applied separately to each heartbeat to extract corresponding coefficients, which are categorized as 'morphological' features. In addition, RR interval information is also obtained characterizing the 'rhythm' around the corresponding heartbeat providing 'dynamic' features. These two different types of features are then concatenated and Support Vector Machine (SVM) is utilized for the classification of heartbeats into 15 classes. The procedure is applied to the data from two ECG leads independently and the two results are fused for the final decision. Compare the two classification results and the classification result is kept if the two are identical or the one with greater classification confidence is picked up if the two are inconsistent. The proposed method was tested over the entire MIT-BIH Arrhythmias Database [1] and it yields an overall accuracy of 99.66% on 85945 heartbeats, better than any other published results.

Abstract
Automatic classification of cancer lesions in tissues observed using gastroenterology imaging is a non-trivial pattern recognition task involving filtering, segmentation, feature extraction and classification. In this paper we measure the impact of a variety of segmentation algorithms (mean shift, normalized cuts, level-sets) on the automatic classification performance of gastric tissue into three classes: cancerous, precancerous and normal. Classification uses a combination of color (hue-saturation histograms) and texture (local binary patterns) features, applied to two distinct imaging modalities: chromoendoscopy and narrow-band imaging. Results show that mean-shift obtains an interesting performance for both scenarios producing low classification degradations (6%), full image classification is highly inaccurate reinforcing the importance of segmentation research for Gastroenterology, and confirm that Patch Index is an interesting measure of the classification potential of small to medium segmented regions.

Abstract
Although there is a growing number of scientific papers describing classification of in-body images, most of it is based on traditional colour histograms. In this paper we explain why these might not be the most adequate visual features for in-body image classification. Based on a colour dynamic range maximization criterion, we propose a methodology for creating more adequate colour histograms, testing it on a vital-stained magnification endoscopy scenario.