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
Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation
Amir Syahmi, Xiangrong Lu, Yinxuan Li, Haoxuan Yao, Hanjun Jiang, Ishita Acharya, Shiyi Wang, Yang Nan, Xiaodan Xing, Guang Yang
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images
Mazen Soufi, Yoshito Otake, Makoto Iwasa, Keisuke Uemura, Tomoki Hakotani, Masahiro Hashimoto, Yoshitake Yamada, Minoru Yamada, Yoichi Yokoyama, Masahiro Jinzaki, Suzushi Kusano, Masaki Takao, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato
SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
Sihan Yang, Haixia Bi, Hai Zhang, Jian Sun
SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models
Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer, Stefanie Speidel
HYDEN: Hyperbolic Density Representations for Medical Images and Reports
Zhi Qiao, Linbin Han, Xiantong Zhen, Jia-Hong Gao, Zhen Qian