iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models

• URL: http://arxiv.org/abs/2605.05761v1
• Date: Thu, 07 May 2026 06:55:44 GMT
• Title: iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models
• Abstract summary: iTRIALSPACE is a programmable evaluation framework for controlled assessment of lung CT models.<n>We evaluate iTRIALSPACE in a 55,469-sample Virtual Lesion Study spanning three medical VLMs, four spatialguidance conditions, and three clinical tasks.
• Score: 0.8477463841895917
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce iTRIALSPACE, a programmable evaluation framework for controlled assessment of lung CT models. Standard benchmarks are static retrospective collections that entangle lesion size, lobe prevalence, anatomy, and acquisition context, making it difficult to determine what structurally drives model accuracy. iTRIALSPACE addresses this limitation by composing real clinical CTs and lesion profiles into controlled virtual lesion trials through a four-stage pipeline: multidataset nodule profiling, explicit trial specification, anatomy-aware mask insertion, and ControlNet-conditioned CT synthesis. The framework is built on a unified 54-attribute nodule-profile dataset spanning 13,140 annotated nodules from seven public CT sources and instantiated as 13 trial modes. We evaluate iTRIALSPACE in a 55,469-sample Virtual Lesion Study spanning three medical VLMs, four spatialguidance conditions, and three clinical tasks. Across all 13 modes, the synthetic substrate remains within the real-to-real FID baseline, and synthetic performance rankings transfer strongly to real clinical data ($ρ$ = 0.93, p < 10$^{-15}$). Controlled trial modes expose findings unavailable to fixed-distribution benchmarks, including shortcut-driven size prediction collapse under lobe-equalized sampling and hostto-donor variance ratios of 8.9x and 3.3x in twin-cross analysis. These results position iTRIALSPACE as an auditable evaluation infrastructure for controlled, falsifiable testing beyond static retrospective benchmarks.

Related papers

• Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided [62.10276009853225]
  Reliable pelvic bone segmentation from CT is essential for robot-assisted pelvic trauma surgery.<n>Existing methods show limited effectiveness for PBS.<n>We propose a novel closed-loop dynamic Reliability-Guided framework (ReGA) for PBS.
  arXiv  Detail & Related papers  (2026-08-01T08:13:20Z)
• CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training [2.2462804525190427]
  Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes.<n>We present CONFLUX, a latent diffusion model for chest computed tomography (CT)<n>A 3D variational autoencoder compresses each volume, and a rectified-flow transformer generates in the latent space.
  arXiv  Detail & Related papers  (2026-07-03T06:31:00Z)
• Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution [6.049628621178308]
  We propose a cross-modal method that projects feature attributions from high-performance 12-lead ECG models onto the CineECG 3D anatomical space.<n>Our study reveals that while models trained directly on CineECG signals suffer from reduced accuracy and incoherent attributions, the proposed mapping mechanism effectively recovers clinically relevant feature rankings.
  arXiv  Detail & Related papers  (2026-04-29T12:21:43Z)
• AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning [0.0]
  We propose AnyCXR, a unified framework that enables generalizable multi-organ segmentation across arbitrary CXR projection angles.<n>We trained AnyCXR entirely on synthetic data, providing accurate delineation of 54 anatomical structures in PA, lateral, and oblique views.<n>Results demonstrate that AnyCXR establishes a scalable and reliable foundation for anatomy-aware CXR analysis.
  arXiv  Detail & Related papers  (2025-12-19T06:27:14Z)
• Conformal Lesion Segmentation for 3D Medical Images [82.92159832699583]
  We propose a risk-constrained framework that calibrates data-driven thresholds via conformalization to ensure the test-time FNR remains below a target tolerance.<n>We validate the statistical soundness and predictive performance of CLS on six 3D-LS datasets across five backbone models, and conclude with actionable insights for deploying risk-aware segmentation in clinical practice.
  arXiv  Detail & Related papers  (2025-10-19T08:21:00Z)
• Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach [57.86418347491272]
  We propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions.<n>We contribute a dataset containing over 14.5K CT images from multiple planes and all human body regions, and meticulously provide grounding annotations for over 19K abnormalities.<n>We propose OminiAbnorm-CT, which can automatically ground and describe abnormal findings on multi-plane and whole-body CT images based on text queries.
  arXiv  Detail & Related papers  (2025-06-03T17:57:34Z)
• 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)
• TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models [21.437563965711004]
  We present TRIALSCOPE, a framework designed to generate robust real-world evidence from observational data at scale.<n>The framework was shown to automatically curate high-quality structured patient data, expanding the dataset and incorporating key patient attributes only available in unstructured form.<n>We were also able to show that TRIALSCOPE could reproduce results of lung and pancreatic cancer clinical trials from the extracted real world data.
  arXiv  Detail & Related papers  (2023-11-02T15:15:47Z)
• Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines [113.08940153125616]
  We generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing comprehensive anatomical coverage. Our proposed procedure does not rely on manual annotation during the label aggregation stage. We release our trained unified anatomical segmentation model capable of predicting $142$ anatomical structures on CT data.
  arXiv  Detail & Related papers  (2023-07-25T09:48:13Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Adversarial Transformer for Repairing Human Airway Segmentation [7.176060570019899]
  This paper presents a patch-scale adversarial-based refinement network that takes in preliminary segmentation along with original CT images and outputs a refined mask of the airway structure. The results are quantitatively evaluated by seven metrics and achieved more than a 15% rise in detected length ratio and detected branch ratio.
  arXiv  Detail & Related papers  (2022-10-21T15:20:08Z)
• 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)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)

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.