LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning

• URL: http://arxiv.org/abs/2407.13217v1
• Date: Thu, 18 Jul 2024 07:00:23 GMT
• Title: LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning
• Authors: Wei Huang, Wei Liu, Xiaoming Zhang, Xiaoli Yin, Xu Han, Chunli Li, Yuan Gao, Yu Shi, Le Lu, Ling Zhang, Lei Zhang, Ke Yan,
• Abstract summary: A precise LIver tumor DIAgnosis network on multi-phase contrast-enhance CT, named LIDIA, is proposed for real-world scenario. We constructed a large-scale dataset comprising 1,921 patients and 8,138 lesions. LIDIA has achieved an average AUC of 93.6% across eight different types of lesions, demonstrating its effectiveness.
• Score: 26.628742010072756
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability of liver tumors. In this work, a precise LIver tumor DIAgnosis network on multi-phase contrast-enhance CT, named LIDIA, is proposed for real-world scenario. To fully utilize all available phases in contrast-enhanced CT, LIDIA first employs the iterative fusion module to aggregate variable numbers of image phases, thereby capturing the features of lesions at different phases for better tumor diagnosis. To effectively mitigate the high heterogeneity problem of liver tumors, LIDIA incorporates asymmetric contrastive learning to enhance the discriminability between different classes. To evaluate our method, we constructed a large-scale dataset comprising 1,921 patients and 8,138 lesions. LIDIA has achieved an average AUC of 93.6% across eight different types of lesions, demonstrating its effectiveness. Besides, LIDIA also demonstrated strong generalizability with an average AUC of 89.3% when tested on an external cohort of 828 patients.

Related papers

• 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)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.586530244472655]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network [37.931408083443074]
  Pixel-Lesion-pAtient Network (PLAN) is proposed to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation.
  arXiv  Detail & Related papers  (2023-07-17T06:21:45Z)
• A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans [15.352636778576171]
  Liver cancer has high morbidity and mortality rates in the world. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. In this work, we customize a multi-object labeling tool for multi-phase CT images.
  arXiv  Detail & Related papers  (2023-06-28T09:11:34Z)
• CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans [45.83431075462771]
  Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice. Most medical AI systems are built to focus on single organs with a narrow list of a few diseases. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction.
  arXiv  Detail & Related papers  (2023-01-28T20:09:34Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology [26.194247664756553]
  Intraoperative diagnosis of skull base tumors can be challenging due to tumor diversity and lack of intraoperative pathology resources. We developed an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses.
  arXiv  Detail & Related papers  (2021-08-08T02:49:29Z)
• Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort [43.99203831722203]
  Our retrospective study obtained 2096 chest CTs from 16 institutions. A metric-based approach for classification of COVID-19 used interpretable features. A deep learning-based classifier differentiated COVID-19 via 3D features extracted from CT attenuation and probability distribution of airspace opacities.
  arXiv  Detail & Related papers  (2020-06-09T00:40:35Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)

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.