Chest-OMDL: Organ-specific Multidisease Detection and Localization in Chest Computed Tomography using Weakly Supervised Deep Learning from Free-text Radiology Report

Xuguang Bai, Mingxuan Liu, Yifei Chen, Hongjia Yang, Qiyuan Tian
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:59-81, 2026.

Abstract

Deep learning (DL) models designed to detect abnormalities in chest computed tomography (CT) reduce radiologists’ workload. However, training multidisease diagnostic models requires large expert-annotated datasets, significantly increasing model development cost. To address this challenge, we propose a weakly supervised learning (WSL) framework entitled Chest-OMDL for Organ-specific Multidisease Detection and Localization in chest CT. Chest-OMDL trains DL models using disease labels extracted by RadBERT from free-text radiology reports and multi-organ segmentation masks generated by the Segment Anything by Text (SAT) model, therefore reducing the need for manual annotation. Specifically, Chest-OMDL employs a Y-shaped Mamba model (Y-Mamba), comprising a feature extractor, an organ segmentation decoder, and a disease anomaly map generator. By incorporating multidisease anatomical knowledge, Y-Mamba is trained with a multi-task loss for organ-level weak supervision. Chest-OMDL was trained and validated on the large-scale CT-RATE dataset (25,692 non-contrast 3D chest CT scans from 21,304 patients) and tested on the external RAD-ChestCT dataset (3,630 scans), outperforming CT-CLIP (contrastive language-image pre-training) and CT-Net (full supervision). Code: \url{https://github.com/JasonW375/Chest-OMDL}

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-bai26a, title = {Chest-OMDL: Organ-specific Multidisease Detection and Localization in Chest Computed Tomography using Weakly Supervised Deep Learning from Free-text Radiology Report}, author = {Bai, Xuguang and Liu, Mingxuan and Chen, Yifei and Yang, Hongjia and Tian, Qiyuan}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {59--81}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/bai26a/bai26a.pdf}, url = {https://proceedings.mlr.press/v301/bai26a.html}, abstract = {Deep learning (DL) models designed to detect abnormalities in chest computed tomography (CT) reduce radiologists’ workload. However, training multidisease diagnostic models requires large expert-annotated datasets, significantly increasing model development cost. To address this challenge, we propose a weakly supervised learning (WSL) framework entitled Chest-OMDL for Organ-specific Multidisease Detection and Localization in chest CT. Chest-OMDL trains DL models using disease labels extracted by RadBERT from free-text radiology reports and multi-organ segmentation masks generated by the Segment Anything by Text (SAT) model, therefore reducing the need for manual annotation. Specifically, Chest-OMDL employs a Y-shaped Mamba model (Y-Mamba), comprising a feature extractor, an organ segmentation decoder, and a disease anomaly map generator. By incorporating multidisease anatomical knowledge, Y-Mamba is trained with a multi-task loss for organ-level weak supervision. Chest-OMDL was trained and validated on the large-scale CT-RATE dataset (25,692 non-contrast 3D chest CT scans from 21,304 patients) and tested on the external RAD-ChestCT dataset (3,630 scans), outperforming CT-CLIP (contrastive language-image pre-training) and CT-Net (full supervision). Code: \url{https://github.com/JasonW375/Chest-OMDL}} }
Endnote
%0 Conference Paper %T Chest-OMDL: Organ-specific Multidisease Detection and Localization in Chest Computed Tomography using Weakly Supervised Deep Learning from Free-text Radiology Report %A Xuguang Bai %A Mingxuan Liu %A Yifei Chen %A Hongjia Yang %A Qiyuan Tian %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-bai26a %I PMLR %P 59--81 %U https://proceedings.mlr.press/v301/bai26a.html %V 301 %X Deep learning (DL) models designed to detect abnormalities in chest computed tomography (CT) reduce radiologists’ workload. However, training multidisease diagnostic models requires large expert-annotated datasets, significantly increasing model development cost. To address this challenge, we propose a weakly supervised learning (WSL) framework entitled Chest-OMDL for Organ-specific Multidisease Detection and Localization in chest CT. Chest-OMDL trains DL models using disease labels extracted by RadBERT from free-text radiology reports and multi-organ segmentation masks generated by the Segment Anything by Text (SAT) model, therefore reducing the need for manual annotation. Specifically, Chest-OMDL employs a Y-shaped Mamba model (Y-Mamba), comprising a feature extractor, an organ segmentation decoder, and a disease anomaly map generator. By incorporating multidisease anatomical knowledge, Y-Mamba is trained with a multi-task loss for organ-level weak supervision. Chest-OMDL was trained and validated on the large-scale CT-RATE dataset (25,692 non-contrast 3D chest CT scans from 21,304 patients) and tested on the external RAD-ChestCT dataset (3,630 scans), outperforming CT-CLIP (contrastive language-image pre-training) and CT-Net (full supervision). Code: \url{https://github.com/JasonW375/Chest-OMDL}
APA
Bai, X., Liu, M., Chen, Y., Yang, H. & Tian, Q.. (2026). Chest-OMDL: Organ-specific Multidisease Detection and Localization in Chest Computed Tomography using Weakly Supervised Deep Learning from Free-text Radiology Report. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:59-81 Available from https://proceedings.mlr.press/v301/bai26a.html.

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