Medical Image
Medical image analysis focuses on extracting meaningful information from various imaging modalities (e.g., CT, MRI, X-ray) to improve diagnosis and treatment planning. Current research emphasizes developing robust and efficient algorithms, often employing convolutional neural networks (CNNs), transformers, and diffusion models, to address challenges like data variability, limited annotations, and privacy concerns. These advancements are crucial for improving the accuracy and speed of medical image analysis, leading to better patient care and accelerating medical research.
Papers
A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning
Jing Wang, Junyan Fan, Meng Zhou, Yanzhu Zhang, Mingyu Shi
Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description
Xiaohan Cheng, Taiyuan Mei, Yun Zi, Qi Wang, Zijun Gao, Haowei Yang
Exploring connections of spectral analysis and transfer learning in medical imaging
Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor, Veronika Cheplygina
LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task
Khai Le-Duc, Ryan Zhang, Ngoc Son Nguyen, Tan-Hanh Pham, Anh Dao, Ba Hung Ngo, Anh Totti Nguyen, Truong-Son Hy
Pay Less On Clinical Images: Asymmetric Multi-Modal Fusion Method For Efficient Multi-Label Skin Lesion Classification
Peng Tang, Tobias Lasser
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models
Shanghao Shi, Md Shahedul Haque, Abhijeet Parida, Marius George Linguraru, Y. Thomas Hou, Syed Muhammad Anwar, Wenjing Lou
Automating Weak Label Generation for Data Programming with Clinicians in the Loop
Jean Park, Sydney Pugh, Kaustubh Sridhar, Mengyu Liu, Navish Yarna, Ramneet Kaur, Souradeep Dutta, Elena Bernardis, Oleg Sokolsky, Insup Lee
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Wei Dai, Rui Liu, Zixuan Wu, Tianyi Wu, Min Wang, Junxian Zhou, Yixuan Yuan, Jun Liu