CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Electrical Engineering and Systems Science > Image and Video Processing
Title:CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs
Abstract:We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract informative features from specific anatomical regions. Furthermore, it captures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accurate, effective and efficient retrieval, particularly for less prevalent findings. CheXtriv outperforms state-of-the-art global and local approaches by 18% to 26% in retrieval accuracy and 11% to 23% in ranking quality. The code is available at this https URL.
| Comments: | Accepted at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024) |
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.28137 [eess.IV] |
| (or arXiv:2608.28137v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.28137
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Sparse Priors for Efficient Distribution Learning
Sep 21
-
Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding
Sep 21
-
Bio-MF: Low-Latency and High-Fidelity EEG-to-fNIRS Cross-Modal Generation for Hybrid Motor-Imagery Brain--Computer Interfaces
Sep 21
-
Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies
Sep 21
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.