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Found 5 papers, 1 papers with code
SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage.
FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning
This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views, reducing or eliminating the need for manual labeling.
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models
The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians.
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation
In this work, we propose a weakly supervised machine learning approach that learns from only ceT1 scans and adapts to segment two structures from hrT2 scans: the VS and the cochlea from the crossMoDA dataset.
Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?
Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types.
