Organ-Aware Attention Improves CT Triage and Classification

• URL: http://arxiv.org/abs/2601.13385v1
• Date: Mon, 19 Jan 2026 20:37:45 GMT
• Title: Organ-Aware Attention Improves CT Triage and Classification
• Authors: Lavsen Dahal, Yubraj Bhandari, Geoffrey D. Rubin, Joseph Y. Lo,
• Abstract summary: Off-the-shelf Vision Language Models struggle with 3D anatomy, protocol shifts, and noisy report supervision.<n>This study used the two largest publicly available chest CT datasets: CT-RATE and RADCHEST-CT.<n>We present ORACLE-CT, an encoder-agnostic, organ-aware head that pairs Organ-Masked Attention with Organ-Scalar Fusion.
• Score: 0.7901846308308808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is an urgent need for triage and classification of high-volume medical imaging modalities such as computed tomography (CT), which can improve patient care and mitigate radiologist burnout. Study-level CT triage requires calibrated predictions with localized evidence; however, off-the-shelf Vision Language Models (VLM) struggle with 3D anatomy, protocol shifts, and noisy report supervision. This study used the two largest publicly available chest CT datasets: CT-RATE and RADCHEST-CT (held-out external test set). Our carefully tuned supervised baseline (instantiated as a simple Global Average Pooling head) establishes a new supervised state of the art, surpassing all reported linear-probe VLMs. Building on this baseline, we present ORACLE-CT, an encoder-agnostic, organ-aware head that pairs Organ-Masked Attention (mask-restricted, per-organ pooling that yields spatial evidence) with Organ-Scalar Fusion (lightweight fusion of normalized volume and mean-HU cues). In the chest setting, ORACLE-CT masked attention model achieves AUROC 0.86 on CT-RATE; in the abdomen setting, on MERLIN (30 findings), our supervised baseline exceeds a reproduced zero-shot VLM baseline obtained by running publicly released weights through our pipeline, and adding masked attention plus scalar fusion further improves performance to AUROC 0.85. Together, these results deliver state-of-the-art supervised classification performance across both chest and abdomen CT under a unified evaluation protocol. The source code is available at https://github.com/lavsendahal/oracle-ct.

Related papers

• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography [13.885760158090692]
  Myocardial Region-guided Feature Aggregation Net is a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation.<n>Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via bridging expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features.
  arXiv  Detail & Related papers  (2025-04-27T16:43:52Z)
• High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation [7.2151442883882]
  We propose a 3D image segmentation algorithm for automated pulmonary vessel segmentation from both contrast-enhanced and non-contrast CT images.<n>We used 427 sets of high-precision annotated CT data from multiple vendors and countries to train the model and achieved Cl-DICE, Cl-Recall, and Recall values of 0.892, 0.861, 0.924 for PulmonaryA data and 0.925, 0.903, 0.949 for NCCT data.
  arXiv  Detail & Related papers  (2025-03-21T09:54:42Z)
• RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis [56.57177181778517]
  RadGenome-Chest CT is a large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. We leverage the latest powerful universal segmentation and large language models to extend the original datasets.
  arXiv  Detail & Related papers  (2024-04-25T17:11:37Z)
• Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet [0.5106162890866905]
  We propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches. We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80.
  arXiv  Detail & Related papers  (2023-08-06T03:28:08Z)
• Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography [48.7576911714538]
  We developed a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field OCT scans. Our classification algorithm was able to segment ONH and macular tissues with a DC of 0.94 $pm$ 0.003. This may encourage the mainstream adoption of 3D wide-field OCT scans.
  arXiv  Detail & Related papers  (2022-10-13T01:51:29Z)
• CACTUSS: Common Anatomical CT-US Space for US examinations [36.45569352490318]
  Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Recent abdominal CT datasets have been successfully utilized to train deep neural networks for automatic aorta segmentation. CACTUSS acts as a virtual bridge between CT and US modalities to enable automatic AAA screening sonography.
  arXiv  Detail & Related papers  (2022-07-18T14:05:25Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation [5.457168581192045]
  Recent deep learning algorithms produced promising vascular segmentation results. However, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality.
  arXiv  Detail & Related papers  (2021-07-09T07:51:33Z)
• Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation [53.187152856583396]
  We propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features.
  arXiv  Detail & Related papers  (2020-10-09T08:22:54Z)
• Deep Reinforcement Learning for Organ Localization in CT [59.23083161858951]
  We propose a deep reinforcement learning approach for organ localization in CT. In this work, an artificial agent is actively self-taught to localize organs in CT by learning from its asserts and mistakes. Our method can use as a plug-and-play module for localizing any organ of interest.
  arXiv  Detail & Related papers  (2020-05-11T10:06:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.