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Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning
Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis.
A scoping review on multimodal deep learning in biomedical images and texts
In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research.
T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator
Image classification is one of the most fundamental tasks in Computer Vision.
GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression
Besides the latent variable unique to each patch, we introduce shared latent variables between patches to construct the global context.
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