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Found 10 papers, 2 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.
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future
With more than 50 papers included in this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis.
End-to-end detection-segmentation network with ROI convolution
We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit.
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
We propose an attention mechanism for 3D medical image segmentation.
