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Found 5 papers, 1 papers with code
Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD
To address this gap, we develop a computer-aided diagnostic model focusing on white matter regions in brain MRI by employing radiomics and machine learning methods.
A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features
The results shows that our proposed UNet-SVE-VGG-MLP model has the optimal performance for classifying diseases related to retinal fundus images on the STARE database, with a overall accuracy of 99. 96% and a weighted AUC of 99. 98% for the 14 categories on test dataset.
DAFFNet: A Dual Attention Feature Fusion Network for Classification of White Blood Cells
In this study, we propose a novel dual-branch network Dual Attention Feature Fusion Network (DAFFNet), which for the first time integrates the high-level semantic features with morphological features of WBC to achieve accurate classification.
Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier
Additionally, We find that most of the solutions to long-tailed problems are still biased towards head classes in the end, and we propose a simple and post hoc prediction re-balancing strategy to further mitigate the basis toward head class.
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
This paper proposes a new depression detection system based on LLMs that is both interpretable and interactive.
