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Found 6 papers, 1 papers with code
A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation
Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, and domain-specific restriction limits model generalizability.
Application Of Vision-Language Models For Assessing Osteoarthritis Disease Severity
Osteoarthritis (OA) poses a global health challenge, demanding precise diagnostic methods.
Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children
However, their performance highly relies on the quality and quantity of the data annotation.
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function.
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation
The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset.
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)
The iMaskRCNN led to improved bone and cartilage segmentation compared to Mask RCNN as indicated with the increase in dice score from 95% to 98% for the femur, 95% to 97% for tibia, 71% to 80% for femoral cartilage, and 81% to 82% for tibial cartilage.
