Abstract: This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation in medical ...

Deep learning has been successfully applied in the field of medical diagnosis, and improving the accurate classification of MRI images through deep learning is important for early treatment and ...

This work is part of the Software Project: "Language, Action and Perception" at Saarland University, WS 2020-2021. This repository contains our implementation and a summary of our research findings.

TL;DR: We propose CUFIT, a robust fine-tuning method for vision foundation models under noisy label conditions, based on the advantages of linear probing and adapters. Download the training data, ...

This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision Transformers (ViT) have emerged as a promising option ...

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that ...

Artificial intelligence (AI) holds the promise for more objective, accurate, and cost-effective analysis of imaging data and could fundamentally transform clinical workflows in image-based diagnostics ...

Non-invasive glioma grade classification is an exciting area in neuroimaging. The primary purpose of this study is to investigate the performance of different medical image fusion algorithms for ...

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of ...

cDepartment of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China dDepartment of Radiation Oncology, The First Affiliated Hospital of Guangzhou Medical ...