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Found 7 papers, 1 papers with code
Domain Aligned CLIP for Few-shot Classification
Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from inter-modal (image-text) alignment via contrastive objectives.
A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries.
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.
Unified Image and Video Saliency Modeling
We evaluate our method on the video saliency datasets DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and MIT300.
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.
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.
