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 ...
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image ...
YOLOv8-Seg: a deep learning approach for accurate classification of osteoporotic vertebral fractures
This study investigates the application of a deep learning model, YOLOv8-Seg, for the automated classification of osteoporotic vertebral fractures (OVFs) from computed tomography (CT) images. A ...
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.
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
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 ...
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 ...
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