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
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
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang, Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan, Errol Colak, Adewole Adamson, Laura Heacock, Geoffrey H. Tison, Alex Tamkin, Pranav Rajpurkar
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation
Chuanfei Hu, Tianyi Xia, Shenghong Ju, Xinde Li
Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
Ye Zhu, Jie Yang, Si-Qi Liu, Ruimao Zhang
Removing confounding information from fetal ultrasound images
Kamil Mikolaj, Manxi Lin, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Tolsgaard, Anders Nymark, Aasa Feragen
Leveraging Old Knowledge to Continually Learn New Classes in Medical Images
Evelyn Chee, Mong Li Lee, Wynne Hsu