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
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
INTRABENCH: Interactive Radiological 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
Parameter choices in HaarPSI for IQA with medical images
Clemens Karner, Janek Gröhl, Ian Selby, Judith Babar, Jake Beckford, Thomas R Else, Timothy J Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, Michael Roberts, James H.F. Rudd, Carola-Bibiane Schönlieb, Jonathan R Weir-McCall, Anna Breger
Denoising Diffusion Models for Anomaly Localization in Medical Images
Cosmin I. Bercea, Philippe C. Cattin, Julia A. Schnabel, Julia Wolleb