Titlebook: Cancer Prevention Through Early Detection; First International Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202

TEM

Comparing Training Strategies Using Multi-Assessor Segmentation Labels for?Barrett’s Neoplasia Detecection. The value used to generate this curve is the maximum pixel value in the raw segmentation map, and the histologically proven ground truth of the image. The experiments show that random sampling of the four neoplastic areas together with a compound loss Binary Cross-entropy and DICE yields the

舔食

Improved Pancreatic Tumor Detection by?Utilizing Clinically-Relevant Secondary Features based on a U-Net-like Deep CNN that exploits the following external secondary features: the pancreatic duct, common bile duct and the pancreas, along with a processed CT scan. Using these features, the model segments the pancreatic tumor if it is present. This segmentation for classification and lo

GUILE

精致

3D-Morphomics, Morphological Features on?CT Scans for?Lung Nodule Malignancy Diagnosis The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ’s surface is developed, and coupled with an automatic extraction of morphologi

Cloudburst

Self-supervised Approach for?a?Fully Assistive Esophageal Surveillance: Quality, Anatomy and?Neoplasgnosis and treatment. While endoscopic videos are corrupted with multiple artefacts and procedure require investigating extended areas such as stomach, it is inevitable that there is risk of missing areas that may potentially harbour neoplastic changes and require immediate attention. A complete gui

habile

CLAN

解凍

Lightweight Transformer Backbone for?Medical Object Detectiong tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algorithms are likely to fail when applied to this task. In this paper, we present a practical yet lightweight backbone to improve the accuracy of tumor detection. Specifically, we propose a novel m

代理人

Contrastive and?Attention-Based Multiple Instance Learning for?the?Prediction of?Sentinel Lymph Nodet at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited da

V洗浴

Knowledge Distillation with?a?Class-Aware Loss for?Endoscopic Disease Detectionion is providing crucial diagnostic support, however, subtle lesions in upper and lower GI are quite hard to detect and cause considerable missed detection. In this work, we leverage deep learning to develop a framework to improve the localization of difficult to detect lesions and minimize the miss