Back
Enhancing Fetal Cardiac Ultrasound Diagnosis: A Multi-Task Hybrid Attention Model for Accurate Standard Plane Detection
Tian, H.; Au, F.
2024-09-06
cardiovascular medicine
10.1101/2024.09.05.24313076
medRxiv
Show abstract
Withdrawal StatementThe authors have withdrawn this manuscript because it contains fundamental errors and fabricated data. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Matching journals
●Non-profit
◐University press
○Commercial
The top 6 journals account for 50% of the predicted probability mass.
1
IEEE Transactions on Biomedical Engineering
●
40 papers in training set
Top 0.1%
18.7%
Similar papers in this journal
- Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries 94%
- A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture 94%
- Passive Acoustic Dynamic Differentiation and Mapping: A Time-Domain Passive Cavitation Localization and Classification Approach 93%
2
IEEE Journal of Biomedical and Health Informatics
●
37 papers in training set
Top 0.1%
12.0%
Similar papers in this journal
3
Biomedical Signal Processing and Control
○
22 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
4
IEEE Access
●
35 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
- A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning 94%
- Self-Supervised Electrocardiograph De-noising 93%
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 93%
6
Biomedical Optics Express
●
95 papers in training set
Top 0.3%
4.9%
Similar papers in this journal
- Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation 92%
- A compact breast shape acquisition system for improving diffuse optical tomography image reconstructions 91%
- Leveraging Pretrained Vision Transformers for Automated Cancer Diagnosis in Optical Coherence Tomography Images 91%
50% of probability mass above
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.