Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)

@article{Felfeliyan2021ImprovedMaskRT,
  title={Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)},
  author={Banafsheh Felfeliyan and Abhilash Rakkunedeth Hareendranathan and Gregor Kuntze and Jacob Lester Jaremko and Janet Lenore Ronsky},
  journal={Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society},
  year={2021},
  volume={97},
  pages={
          102056
        }
}

MRI Knee Domain Translation for Unsupervised Segmentation By CycleGAN (data from Osteoarthritis initiative (OAI))

A domain adaptation (DA) framework using the CycleGAN model for MRI translation that would aid in unsupervised MRI data segmentation is proposed and is validated on five scans from the Osteoarthritis Initiative (OAI) dataset.

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

The proposed self-supervised pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information and is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification.

Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss

The results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels.

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