eess.IVAug 28, 2026

CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs

Authors: Naren Akash, Arihanth Tadanki, Jayanthi Sivaswamy

Organizations: Center for Visual Information Technology, International Institute of Information Technology Hyderabad, India

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

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 https://github.com/cvit-mip/chextriev.

Figures & tables

Figure 1: A schematic illustration of CheXtriev.
VariantsIRMEdge ConnectivityLocationMLFmAPmHRmRR
V0✕✕✕✕51.839.754.0
V1GTShared Binary✕Global52.939.955.2
V2GTShared Uniform✕Global53.940.856.3
V3GTShared UniformLearnableGlobal54.040.956.4
V4GTUnique Ri−RjR_{i}-R_{j}LearnableLocal51.839.853.7
V5GTUnique Ri−RjR_{i}-R_{j}✕Global54.640.957.1
Table 1: Impact of design choices in CheXtriev on retrieval and ranking performance. Here, IRM and MLF denote inter-anatomic region modelling using graph transformers and multi-level features with gated residuals, respectively.
 Global CNNATH [ 1 ]AnaXNet [ 2 ]CheXtriev
FindingsAPHRRRAPHRRRAPHRRRAPHRRR
LO90.4∗90.4^{\tiny\ast}85.9∗85.9^{\tiny\ast}92.288.2∗88.2^{\tiny\ast}82.2∗82.2^{\tiny\ast}90.988.8∗88.8^{\tiny\ast}82.9∗82.9^{\tiny\ast}91.291.787.693.3
PE68.6∗68.6^{\tiny\ast}54.7∗54.7^{\tiny\ast}72.662.1∗62.1^{\tiny\ast}46.3∗46.3^{\tiny\ast}66.163.7∗63.7^{\tiny\ast}46.2∗46.2^{\tiny\ast}67.271.459.474.7
AT60.3∗60.3^{\tiny\ast}44.1∗44.1^{\tiny\ast}64.254.3∗54.3^{\tiny\ast}36.8∗36.8^{\tiny\ast}57.859.1∗59.1^{\tiny\ast}42.3∗42.3^{\tiny\ast}62.864.449.167.6
ECS64.7∗64.7^{\tiny\ast}48.3∗48.3^{\tiny\ast}67.961.3∗61.3^{\tiny\ast}45.4∗45.4^{\tiny\ast}64.964.7∗64.7^{\tiny\ast}48.5∗48.5^{\tiny\ast}68.469.456.073.7
PE/HO56.5∗56.5^{\tiny\ast}40.2∗40.2^{\tiny\ast}59.251.8∗51.8^{\tiny\ast}34.7∗34.7^{\tiny\ast}54.753.5∗53.5^{\tiny\ast}34.9∗34.9^{\tiny\ast}57.462.547.865.8
Table 2: Comparison of top-5 retrieval performance on MIMIC-CXR dataset against global baselines (CNN, ATH) and a local variant (AnaXNet). (.)∗(.)^{\ast} indicates p<0.05p<0.05 .
Figure 2: Retrieval performance and saliency map analysis for a sample query image with enlarged cardiac silhouette (ECS). Each row displays the top 4 retrieved images and their corresponding occlusion-based saliency maps generated by CheXtriev, AnaXNet, ATH and Global CNN (top to bottom). A retrieved image is considered correct if it matches the specific finding of interest, in this case ECS, as the query image.
 Global CNNATH [ 1 ]AnaXNet [ 2 ]CheXtriev
FindingsAPHRRRAPHRRRAPHRRRAPHRRR
LO91.4 ∗85.8 ∗91.9 ∗89.7 ∗82.5 ∗90.6 ∗90.7 ∗84.0 ∗91.7 ∗92.588.293.0
PE70.0 ∗56.2 ∗71.262.6 ∗47.6 ∗63.9 ∗64.8 ∗48.6 ∗65.7 ∗72.159.473.0
AT60.8 ∗45.2 ∗61.9 ∗53.9 ∗36.8 ∗54.5 ∗59.7 ∗42.5 ∗61.1 ∗64.948.565.8
ECS64.5 ∗49.3 ∗65.6 ∗61.6 ∗45.9 ∗62.5 ∗64.5 ∗48.3 ∗65.2 ∗70.555.471.7
PE/HO55.3 ∗40.7 ∗56.1 ∗51.0 ∗36.0 ∗51.7 ∗53.7 ∗36.3 ∗54.5 ∗64.049.165.2
Table 3: Comparison of top-3 retrieval performance on MIMIC-CXR dataset against global baselines (CNN, ATH) and a local variant (AnaXNet). (.)^{{\color[rgb]{0,0,1}\ast}} indicates p<0.05p<0.05 .
 Global CNNATH [ 1 ]AnaXNet [ 2 ]CheXtriev
FindingsAPHRRRAPHRRRAPHRRRAPHRRR
LO89.0 ∗86.1 ∗92.2 ∗86.2 ∗82.0 ∗90.9 ∗87.3 ∗83.5 ∗91.9 ∗90.587.793.2
PE64.4 ∗54.1 ∗73.258.2 ∗45.2 ∗67.2 ∗59.3 ∗46.4 ∗68.9 ∗67.657.575.1
AT55.7 ∗43.5 ∗65.0 ∗50.5 ∗36.1 ∗59.2 ∗55.1 ∗41.4 ∗65.0 ∗59.747.668.6
ECS59.8 ∗48.5 ∗68.5 ∗57.3 ∗43.8 ∗65.7 ∗59.5 ∗46.1 ∗68.3 ∗64.653.174.1
PE/HO52.3 ∗39.1 ∗60.2 ∗48.4 ∗32.8 ∗56.1 ∗50.2 ∗34.0 ∗59.1 ∗59.246.368.0
Table 4: Comparison of top-10 retrieval performance on MIMIC-CXR dataset against global baselines (CNN, ATH) and a local variant (AnaXNet). (.)^{{\color[rgb]{0,0,1}\ast}} indicates p<0.05p<0.05 .
Radiological Findings#Radiographs
Lung Opacity2679
Pleural Effusion1340
Atelectasis1241
Enlarged Cardiac Silhouette1212
Pulmonary Edema / Hazy Opacity819
Pneumonia643
Table 5: Statistics of the Chest ImageGenome [ 3 ] test dataset, which includes frontal chest radiographs (PA or AP view) with valid bounding box annotations for 18 anatomical regions. The data split is based on the official MIMIC-CXR [ 4 , 5 ] data splits.
More in this topic

Medical Image Retrieval

See all 18 papers

Explore similar work

CardsList

  1. CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography

    Jul 30, 2026Tomer Erez, Moshe Kimhi, Chaim Baskin +1Contrastive LearningMedical Imaging

    Technion – Israel Institute of Technology · Ben-Gurion University of the Negev

  2. RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection

    May 17, 2026Shuchang Ye, Mingyuan Meng, Hao Wang +2Medical ImagingMedical Image Segmentation

    The University of Sydney · Zhongguancun Academy · Shanghai Jiao Tong University +1

  3. CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs

    Jun 7, 2026Sergios Gatidis, Curtis Langlotz, Christian BluethgenRadiology VLMsMedical Image Segmentation

    Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, CA, USA · Department of Radiology, Stanford University, Stanford, CA, USA