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Found 5 papers, 5 papers with code
A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images
We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis.
Ranked #1 on
Muscle Tendon Junction Identification
on deepMTJ
Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning
Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively.
Ranked #1 on
Muscle Tendon Junction Identification
on deepMTJ v1
Semi-Supervised and Task-Driven Data Augmentation
However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process.
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
We evaluate the method for brain structure segmentation in MR images.
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.
