Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

In the field of medical Vision-Language Pretraining (VLP), significant efforts have been
devoted to deriving text and image features from both clinical reports and associated …

This project investigates the human multi-modal behavior identification algorithm utilizing
deep neural networks. According to the characteristics of different modal information …

Autoregressive next-token prediction is a standard pretraining method for large-scale
language models, but its application to vision tasks is hindered by the non-sequential nature …

Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and
traditional dataset creation methods are labor intensive, costly, and require extensive …

In the field of medical image compression, Implicit Neural Representation (INR) networks
have shown remarkable versatility due to their flexible compression ratios, yet they are …

Medical vision-and-language models (MVLMs) have attracted substantial interest due to
their capability to offer a natural language interface for interpreting complex medical data …

Convolutional neural networks and attention mechanisms have greatly benefited remote
sensing change detection (RSCD) because of their outstanding discriminative ability …

Nasopharyngeal carcinoma (NPC) has high metastatic potential and is hard to detect early.
This study aims to develop a deep learning model for NPC diagnosis using optical imagery …

Automatic radiology report generation can significantly benefit the labor-intensive process of
report writing by radiologists, especially for 3D radiographs like CT scans, which are crucial …