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
$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model
Chenglong Wang, Dexuan Li, Sucheng Wang, Chengxiu Zhang, Yida Wang, Yun Liu, Guang Yang
SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
Guoyao Deng, Ke Zou, Kai Ren, Meng Wang, Xuedong Yuan, Sancong Ying, Huazhu Fu
Weakly-supervised positional contrastive learning: application to cirrhosis classification
Emma Sarfati, Alexandre Bône, Marc-Michel Rohé, Pietro Gori, Isabelle Bloch
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification
Md Junaid Mahmood, Pranaw Raj, Divyansh Agarwal, Suruchi Kumari, Pravendra Singh
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
Changjian Shui, Justin Szeto, Raghav Mehta, Douglas L. Arnold, Tal Arbel
Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images
Peng Tang, Yang Nan, Tobias Lasser
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation
Jun Shi, Hongyu Kan, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Liang Qiao, Zhaohui Wang, Hong An, Xudong Xue
See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging
Pradeep Singh, Kishore Babu Nampalle, Uppala Vivek Narayan, Balasubramanian Raman
Taming Detection Transformers for Medical Object Detection
Marc K. Ickler, Michael Baumgartner, Saikat Roy, Tassilo Wald, Klaus H. Maier-Hein