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Found 9 papers, 3 papers with code
Active learning using adaptable task-based prioritisation
In this work, the task predictor is a segmentation network.
Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks
In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure.
DeepReg: a deep learning toolkit for medical image registration
DeepReg (https://github. com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification
For images with unanimous consensus between observers, anatomical classification accuracy was 97. 2% and probe adjustment accuracy was 94. 9%.
Image quality assessment for closed-loop computer-assisted lung ultrasound
The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module.
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
Adversarial Deformation Regularization for Training Image Registration Neural Networks
During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation.
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
Results show a median Dice similarity coefficient of 0. 90 with an interquartile range of 0. 08, with equivalent performance to the three operators (with a Williams' index of 1. 03), and outperforming a U-Net architecture without the need for batch normalisation.
Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms.
