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Found 10 papers, 3 papers with code
HuLP: Human-in-the-Loop for Prognosis
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially when faced with the complexities of missing covariates and outcomes.
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.
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification
Many domain generalization techniques were unsuccessful in learning domain-invariant representations due to the large domain shift.
Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer
Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment.
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.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification
Nuclei segmentation and classification is the first and most crucial step that is utilized for many different microscopy medical analysis applications.
Hyperparameter Optimization for COVID-19 Chest X-Ray Classification
Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron.
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays
Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases.
Self-Supervised Learning for Fine-Grained Image Classification
However, building large scale datasets with such annotations is a mammoth task.
