GitHub

TFM Dataset & Benchmark: Automated Tear Film Break-Up Analysis

Classification (TF-Cls) 'Clear', 'Closed', 'Broken', 'Blur' 6,247 3632 × 2760 4,687:561:999(75%:9%:16%) Object Detection (TF-Det) Inside, Middle, Outside Rings 4,736 ...
IEEE

Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function

Abstract: Multi-label emotion classification (MLEC) for low-resource languages like Arabic faces significant challenges due to class imbalance and label correlations, particularly in accurately ...
Frontiers

DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification

Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and ...
IEEE

Learning Compact Semantic Information and Reliable Pseudo-labels for Incomplete Multi-View Multi-Label Classification

Abstract: Multi-view data encompasses various data types, including multi-feature, multi-sequence, and multi-modal data. Multi-view multi-label classification aims to leverage the rich semantic ...