bibtext: @InProceedings{ Aka_CheXtriev_MICCAI2024,
                   author = { Akash R.  J., Naren and Tadanki, Arihanth and Sivaswamy, Jayanthi },
                   title = { { CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
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.
title: CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs
authors: Akash R.  J., Naren Tadanki, Arihanth Sivaswamy, Jayanthi