A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

• URL: http://arxiv.org/abs/2606.16991v1
• Date: Mon, 15 Jun 2026 17:29:32 GMT
• Title: A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
• Abstract summary: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization.<n>It carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.<n>We introduce a novel multi-center benchmark for multi-organ abdominal disease diagnosis and automated radiology report generation.
• Score: 15.152662172359355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload. To address these challenges, we introduce a novel multi-center benchmark for multi-organ abdominal disease diagnosis and automated radiology report generation, which learns to synthesize contrast-enhanced findings from single-phase non-contrast CT (NCCT). To support this, we curated a large-scale dataset of paired NCCT-CECT studies and their corresponding contrast-enhanced radiology reports from two centers, partitioned into internal sets and an external validation cohort. Under a unified evaluation protocol, we benchmarked five contemporary deep learning architectures encompassing chest-specific, abdomen-specific, and general-purpose multimodal domains. Extensive experiments demonstrate that NCCT retains diagnostic signals, achieving an average multi-organ AUC of 69.1% on the internal cohort and 63.1% on the external cohort, respectively. By releasing this dataset and standardized benchmark publicly, this study aims to catalyze future research into safer, resource-efficient, and globally accessible contrast-free abdominal imaging workflows. Code is available at: https://github.com/xmed-lab/TriALS-Report.

Related papers

• TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT [49.93685997940356]
  We describe the TriALS challenge for automated liver lesion segmentation under contrast-limited conditions.<n>The top-performing method achieved a mean venous-phase Dice of 0.754, consistent with human-level performance, yet dropped to 0.57 on NCCT.<n>Algorithm performance was most strongly predicted by training data scale and pre-training strategy.
  arXiv  Detail & Related papers  (2026-05-15T19:23:35Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• Multimodal Deep Learning for Phyllodes Tumor Classification from Ultrasound and Clinical Data [0.29981448312652675]
  Phyllodes tumors (PTs) are difficult to classify preoperatively due to their radiological similarity to benign fibroadenomas.<n>We propose a multimodal deep learning framework that integrates breast ultrasound (BUS) images with structured clinical data to improve diagnostic accuracy.
  arXiv  Detail & Related papers  (2025-08-29T19:54:11Z)
• 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)
• Improved Esophageal Varices Assessment from Non-Contrast CT Scans [15.648325577912608]
  Esophageal varices (EV) is a serious health concern resulting from portal hypertension. Despite non-contrast computed tomography (NC-CT) imaging being a less expensive and non-invasive imaging modality, it has yet to gain full acceptance as a primary clinical diagnostic tool for EV evaluation. We present the Multi-Organ-cOhesion-Network (MOON), a novel framework enhancing the analysis of critical organ features in NC-CT scans for effective assessment of EV.
  arXiv  Detail & Related papers  (2024-07-18T06:49:10Z)
• Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation [21.792652078631868]
  We propose an unsupervised approach to compute non-contrast segmentation without ground-truth label. Unlike generative adversarial approaches, we compute the pairwise morphological context with CECT to provide teacher guidance. We validate our approach on multi-organ segmentation with paired non-contrast & contrast-enhanced CT scans.
  arXiv  Detail & Related papers  (2022-05-12T06:36:33Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble [77.5625174267105]
  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture. We suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection.
  arXiv  Detail & Related papers  (2020-03-18T19:06:27Z)
• Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives [7.036733782879497]
  A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. Cross-validation on open datasets presents the risk of indirect knowledge contamination and could result in circular reasoning.
  arXiv  Detail & Related papers  (2020-02-10T21:49:42Z)

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.