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Found 5 papers, 3 papers with code
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
However, the powerful Mixup sample-mixing data augmentation cannot be well utilized to address this challenge, as it cannot perform linear interpolation on the unknown labels to construct augmented samples.
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement
Accurate classification of laryngeal vascular as benign or malignant is crucial for early detection of laryngeal cancer.
Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image.
Category-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels
A single model submitted to the competition server for the official evaluation achieves mAUC 91. 82% on the test set, which is the highest single model score in the leaderboard and literature.
Ranked #1 on
Multi-Label Classification
on CheXpert
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
To the best of our knowledge, it is the first work to predict the projective transformation matrix as the learning goal for photo rectification.
Ranked #1 on
Medical Image Classification
on CheXphoto
