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
Signature Activation: A Sparse Signal View for Holistic Saliency
Jose Roberto Tello Ayala, Akl C. Fahed, Weiwei Pan, Eugene V. Pomerantsev, Patrick T. Ellinor, Anthony Philippakis, Finale Doshi-Velez
A Systematic Review of Few-Shot Learning in Medical Imaging
Eva Pachetti, Sara Colantonio
Visual Question Answering in the Medical Domain
Louisa Canepa, Sonit Singh, Arcot Sowmya
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations
Rhys Compton, Lily Zhang, Aahlad Puli, Rajesh Ranganath
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models
Michal Byra, Muhammad Febrian Rachmadi, Henrik Skibbe
Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database
Mateja Napravnik, Franko Hržić, Sebastian Tschauner, Ivan Štajduhar
vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images
Mikhail Goncharov, Vera Soboleva, Anvar Kurmukov, Maxim Pisov, Mikhail Belyaev
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Sergio Naval Marimont, Vasilis Siomos, Giacomo Tarroni