First trimester video saliency prediction using cLSTMU-Net with stochastic augmentation
In this paper we develop a multi-modal video analysis algorithm to predict where a sonographer should look next. Our approach uses video and expert knowledge, defined by gaze tracking data, which is acquired during routine first-trimester fetal ultrasound scanning. Specifically, we propose a spatio-...
| Principais autores: | , , , , , |
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| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
IEEE
2022
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