mlsanityData to 14 Oct 2026

Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping

arXiv 2408.15947 ↗First posted 28 Aug 2024eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Dynamic coronary roadmapping is a technology that overlays the vessel maps (the "roadmap") extracted from an offline image sequence of X-ray angiography onto a live stream of X-ray fluoroscopy in real-time. It aims to offer navigational guidance for interventional surgeries without the need for repeated contrast agent injections, thereby reducing the risks associated with radiation exposure and kidney failure. The precision of the roadmaps is contingent upon the accurate alignment of angiographic and fluoroscopic images based on their cardiac phases, as well as precise catheter tip tracking. The former ensures the selection of a roadmap that closely matches the vessel shape in the current frame, while the latter uses catheter tips as reference points to adjust for translational motion between the roadmap and the present vessel tree. Training deep learning models for both tasks is challenging and underexplored. However, incorporating catheter features into the models could offer substantial benefits, given humans heavily rely on catheters to complete the tasks. To this end, we introduce a simple but effective method, auxiliary input in training (AIT), and demonstrate that it enhances model performance across both tasks, outperforming baseline methods in knowledge incorporation and transfer learning.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 28 Aug 2024.

Builds on

18 of 26 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Shortcut Learning in Deep Neural Networkscited by 1,969
  3. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  4. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  5. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  6. 2021Learning Spatio-Temporal Transformer for Visual Trackingcited by 350
  7. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  8. 2018TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rayscited by 139
  9. 2019Attention Based Glaucoma Detection: A Large-scale Database and CNN Modelcited by 76
  10. 2023On the Foundations of Shortcut Learningcited by 65
  11. 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
  12. 2018Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classificationcited by 36
6 more
  1. 2022Teacher-Student Architecture for Knowledge Learning: A Surveycited by 23
  2. 2020Dynamic Coronary Roadmapping via Catheter Tip Tracking in X-ray Fluoroscopy with Deep Learning Based Bayesian Filteringcited by 8
  3. 2019Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Netcited by 6
  4. 2018Deep Neural Networks for ECG-free Cardiac Phase and End-Diastolic Frame Detection on Coronary Angiographiescited by 4
  5. 2023ConTrack: Contextual Transformer for Device Tracking in X-raycited by 4
  6. 2020Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalitiescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Unified Model for Highly Accurate ECG-Free Dynamic Coronary Roadmapping Using Spatio-Temporal Transformerscited by 0