Medical Imaging

Uncertainty-based graph convolutional networks for organ segmentation refinement

Organ segmentation is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, CNNs have dominated the state of the art in this task. Organ segmentation scenarios present a challenging …

A learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks

Colorectal polyps are abnormalities in the colon tissue that can develop into colorectal cancer. The survival rate for patients is higher when the disease is detected at an early stage and polyps can be removed before they develop into malignant …

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art …

Benefit of dual energy CT for lesion localization and classification with convolutional neural networks

Dual Energy CT is a modern imaging technique that is utilized in clinical practice to acquire spectral information for various diagnostic purposes including the identification, classification, and characterization of different liver lesions. It …

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research …

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography

AI meets COVID-19

Brief Progress of Academic | Documentation Dataset Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Pathology Quantification: To be able to quantify the pathologies in thorax CT scans, one needs to segment the pathologies, and probably classify them into common ones characterizing the COVID-19, e.

Preliminary Meeting for the seminar on Federated Learning in Healthcare

Introduction to Federated Learning in Healthcare

Preliminary Meeting for the seminar on Federated Learning in Healthcare

Introduction to Federated Learning in Healthcare

Adaptive image-feature learning for disease classification using inductive graph networks

Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as age and gender to model patient cohort relations in a graph structure. …