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
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification
Dwarikanath Mahapatra
Was that so hard? Estimating human classification difficulty
Morten Rieger Hannemose, Josefine Vilsbøll Sundgaard, Niels Kvorning Ternov, Rasmus R. Paulsen, Anders Nymark Christensen
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W Verjans, Mengyu Wang, Gustavo Carneiro
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
Weina Jin, Xiaoxiao Li, Ghassan Hamarneh
LesionPaste: One-Shot Anomaly Detection for Medical Images
Weikai Huang, Yijin Huang, Xiaoying Tang
Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features
Arman Rahmim, Amirhosein Toosi, Mohammad R. Salmanpour, Natalia Dubljevic, Ian Janzen, Isaac Shiri, Ren Yuan, Cheryl Ho, Habib Zaidi, Calum MacAulay, Carlos Uribe, Fereshteh Yousefirizi
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
Aishik Konwer, Xuan Xu, Joseph Bae, Chao Chen, Prateek Prasanna
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei, Magnus Söderberg, Kevin Smith