Hyderabad: Artificial Intelligence (AI) is transforming the way sleep disorders are diagnosed, with researchers at the ...

Researchers at Osaka Metropolitan University have discovered a practical way to detect and fix common labeling errors in ...

Researchers at Osaka Metropolitan University developed models that classify X-ray images into specific body regions and ...

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...

Abstract: We explore the limitations of traditional crossentropy loss in a hierarchical multi-label classification setting and introduce a novel loss function. This function is designed to integrate ...

aDepartment of Radiology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China bInstitute of High-End Intelligent Health Equipment, Academy of Orthopedics, Guangdong Province ...

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...

I have read the paper and it seemed to be a single-label multi-classification problem. But the code use BCE and sigmoid instead of crossEntropy and softmax. So does it mean that the patient may have ...

Background and objective: Accurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective ...