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
Uterine Ultrasound Image Captioning Using Deep Learning Techniques
Abdennour Boulesnane, Boutheina Mokhtari, Oumnia Rana Segueni, Slimane Segueni
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotations
Mathilde Faanes, Ragnhild Holden Helland, Ole Solheim, Ingerid Reinertsen
Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data
Ayush Singla, Shakson Isaac, Chirag J. Patel
A Survey of Medical Vision-and-Language Applications and Their Techniques
Qi Chen, Ruoshan Zhao, Sinuo Wang, Vu Minh Hieu Phan, Anton van den Hengel, Johan Verjans, Zhibin Liao, Minh-Son To, Yong Xia, Jian Chen, Yutong Xie, Qi Wu
RadioActive: 3D Radiological Interactive Segmentation Benchmark
Constantin Ulrich, Tassilo Wald, Emily Tempus, Maximilian Rokuss, Paul F. Jaeger, Klaus Maier-Hein
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images
Bella Specktor-Fadida, Liat Ben-Sira, Dafna Ben-Bashat, Leo Joskowicz