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Found 12 papers, 2 papers with code
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
To detect OOD samples in this context, the resulting model should generalise to the intra-anatomy variations while rejecting similar OOD samples.
Show from Tell: Audio-Visual Modelling in Clinical Settings
Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings.
Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound
The proposed approach is demonstrated for automated fetal ultrasound imaging tasks, enabling the positive pairs from the same or different ultrasound scans that are anatomically similar to be pulled together and thus improving the representation learning.
Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning
To understand the causal relationship between gaze movement and probe motion, our model exploits multitask learning to jointly learn two related tasks: predicting gaze movements and probe signals that an experienced sonographer would perform in routine obstetric scanning.
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint.
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound
For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer.
Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound
Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88. 8% for goal prediction and 90. 9% for action prediction.
Self-supervised Representation Learning for Ultrasound Video
Therefore, there is significant interest in learning representations from unlabelled raw data.
Discovering Salient Anatomical Landmarks by Predicting Human Gaze
In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images.
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing.
Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images
The feasibility of the approach to produce realistic looking MR images is demonstrated quantitatively and with a qualitative evaluation compared to real fetal MR images.
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters.
