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Found 6 papers, 4 papers with code
TabAttention: Learning Attention Conditionally on Tabular Data
In this paper, we introduce TabAttention, a novel module that enhances the performance of Convolutional Neural Networks (CNNs) with an attention mechanism that is trained conditionally on tabular data.
Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements
We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability.
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery.
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur.
Spontaneous preterm birth prediction using convolutional neural networks
Based on the conducted results and model efficiency, we decided to extend U-Net by adding a parallel branch for classification task.
Estimation of preterm birth markers with U-Net segmentation network
To achieve this goal, we propose to first use a deep neural network architecture for segmenting prenatal ultrasound images and then automatically extract two biophysical ultrasound markers, cervical length (CL) and anterior cervical angle (ACA), from the resulting images.
