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
Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images
Muhammad Usman Akbar, Wuhao Wang, Anders Eklund
eXplainable Artificial Intelligence on Medical Images: A Survey
Matteus Vargas Simão da Silva, Rodrigo Reis Arrais, Jhessica Victoria Santos da Silva, Felipe Souza Tânios, Mateus Antonio Chinelatto, Natalia Backhaus Pereira, Renata De Paris, Lucas Cesar Ferreira Domingos, Rodrigo Dória Villaça, Vitor Lopes Fabris, Nayara Rossi Brito da Silva, Ana Claudia Akemi Matsuki de Faria, Jose Victor Nogueira Alves da Silva, Fabiana Cristina Queiroz de Oliveira Marucci, Francisco Alves de Souza Neto, Danilo Xavier Silva, Vitor Yukio Kondo, Claudio Filipi Gonçalves dos Santos
Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnostics
Ayyub Alzahem, Shahid Latif, Wadii Boulila, Anis Koubaa
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
Dongjie Cheng, Ziyuan Qin, Zekun Jiang, Shaoting Zhang, Qicheng Lao, Kang Li
Segment Anything Model for Medical Images?
Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, Sijing Liu, Haozhe Chi, Xindi Hu, Kejuan Yue, Lei Li, Vicente Grau, Deng-Ping Fan, Fajin Dong, Dong Ni
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging
Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding, Salman Avestimehr
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Sheng He, Rina Bao, Jingpeng Li, Jeffrey Stout, Atle Bjornerud, P. Ellen Grant, Yangming Ou