Lisong Dai

Shanghai Sixth People's Hospital

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Co-Authors

Xiangyang Xu

Huazhong University of Science and Technology

Jianyong Wei

Shanghai Sixth People's Hospital

Yuehua Li

Inner Mongolia Agricultural University

Zheng Sun

Shanghai Sixth People's Hospital

Hongmei Zhou

Quzhou City People's Hospital

Ya Zhang

Shanghai Jiao Tong University

Yueqi Zhu

Shanghai Sixth People's Hospital

Jingxuan Jiang

Shanghai Sixth People's Hospital

Mengfei Wang

National Institute of Clean and Low-Carbon Energy

Chaoyi Wu

Shanghai Artificial Intelligence Laboratory

Cooperative Institutions

Shanghai Jiao Tong University

Shanghai Sixth People's Hospital

Huazhong University of Science and Technology

Soochow University

Chongqing Medical University

Capital Medical University

University of Shanghai for Science and Technology

First Affiliated Hospital of Chongqing Medical University

Shanghai First People's Hospital

Wuhan University

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Improving PI-RADS rating with Zoomed Diffusion-Weighted Imaging in Deep Learning CAD Systems

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024)

Haining Long W.P. Zhu Lei Hu Liming Wei Lisong Dai

Motivation: The clinical accuracy of the Prostate Imaging Reporting and Data System (PI-RADS) rating by deep-learning-based computer-aided diagnosis (DL-CAD) models need further enhancement for improved prostate cancer (PCa) detection and fewer unnecessary biopsies. Goal(s): This study aimed to achieve more precise PI-RADS rating for PCa lesions by using zoomed diffusion-weighted imaging (z-DWI) in DL-CAD models. Approach: We compared the diagnostic performance and PI-RADS rating of DL-CAD using advanced z-DWI vs. conventional DWI and extended this analysis to radiological practice. Results: z-DWI improved the PI-RADS rating of PCa lesions by DL-CAD based on superior diagnostic performance compared with conventional DWI. Impact: Deep-learning-based computer-aided diagnosis using zoomed diffusion-weighted imaging provides more accurate PI-RADS rating than conventional DWI, correlating MRI-detected lesions with prostate cancer (PCa) from biopsy. This can help minimize unnecessary biopsies for benign lesions while facilitating timely PCa treatment.

10.58530/2024/4809

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MD-IQA : Learning Multi-Scale Distributed Image Quality Assessment with Semi Supervised Learning for Low Dose CT

Tao Song Ruizhi Hou Lisong Dai Lei Xiang

10.1109/isbi56570.2024.10635481

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Synchronous Superficial Middle Cerebral Vein Outflow Correlates Favorable Tissue Fate After Mechanical Thrombectomy for Acute Ischemic Stroke

Academic Radiology (2023)

Lisong Dai Zheng Sun Jingxuan Jiang Jianyong Wei Xinyu Song

Outflow

Stroke

10.1016/j.acra.2023.07.005

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Citations (3)

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis

Radiology Artificial Intelligence (2024)

Lei Hu Xiangyu Guo Dawei Zhou Zhen Wang Lisong Dai

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. A targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the Delong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 versus 0.81; P < .001) and 7% at the lesion level (AUPRC: 0.84 versus 0.77; P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% versus -19%; ΔAUPRC: -18% versus -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 versus 0.73; AUPRC: 0.68 versus 0.61), and severe (AUC: 0.75 versus 0.57; AUPRC: 0.69 versus 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based PCa diagnosis, thereby improving its performance in clinical applications. Published under a CC BY 4.0 license.

Artifact (error)

Prostate biopsy

Gold standard (test)

10.1148/ryai.230362

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Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis

IEEE Transactions on Medical Imaging (2021)

Xiaofei Wang Lai Jiang Liu Li Mai Xu Xin Deng

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.

Subnet

Feature (linguistics)

10.1109/tmi.2021.3079709

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Citations (62)

Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study

European Radiology (2024)

Jianyong Wei Kai Shang Xiaoer Wei Yueqi Zhu Yang Yuan

Neuroradiology

Interventional radiology

Stroke

Acute stroke

10.1007/s00330-024-10960-9

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Citations (2)

Development and evaluation of a deep learning model for multi-frequency Gibbs artifact elimination

Quantitative Imaging in Medicine and Surgery (2025)

Lisong Dai Dan Wang Xin Yu Mao Zhenzhuang Miao Lei Lu

Gibbs artifacts frequently occur as a result of truncation in the frequency domain (k-space). Gibbs artifacts can degrade image quality and may be misinterpreted as syrinx, thereby complicating the diagnosis. This study aimed to develop and evaluate a robust deep learning (DL) model that eliminates multi-frequency Gibbs artifacts. We retrospectively collected 290,940 magnetic resonance imaging (MRI) images from 4,936 scans, encompassing 5 anatomical regions and 67 MRI sequences, to develop a DL model for Gibbs artifact removal. This model was trained using artificially generated Gibbs artifacts, featuring various truncation ratios as input data, and its performance in artifact removal was evaluated across different anatomical regions, MRI sequences, and levels of Gibbs artifact severity. For external validation, we prospectively collected data from 20 healthy adults and 10 syrinx patients, comparing radiologists' diagnostic accuracy with area under the receiver operating characteristic curves (AUC) on images before and after artifact removal to assess the model's impact on syrinx diagnosis. The images processed by our model demonstrated a statistically significantly higher image quality score than the original images and those processed by conventional filtering algorithms (all P<0.05). Moreover, the model enables greater confidence in identifying syrinx compared to the original images [AUC: 0.95, 95% confidence interval (CI): 0.92-0.99] versus 0.90 (95% CI: 0.86-0.95) (P=0.04). The model demonstrates excellent performance and robustness in eliminating Gibbs artifacts and may hold the potential for improving syrinx identification.

Artifact (error)

10.21037/qims-24-1344

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Brain structural and functional changes in patients with major depressive disorder: a literature review

PeerJ (2019)

Lisong Dai Hongmei Zhou Xiangyang Xu Zhentao Zuo

Depression is a mental disorder characterized by low mood and anhedonia that involves abnormalities in multiple brain regions and networks. Epidemiological studies demonstrated that depression has become one of the most important diseases affecting human health and longevity. The pathogenesis of the disease has not been fully elucidated. The clinical effect of treatment is not satisfactory in many cases. Neuroimaging studies have provided rich and valuable evidence that psychological symptoms and behavioral deficits in patients with depression are closely related to structural and functional abnormalities in specific areas of the brain. There were morphological differences in several brain regions, including the frontal lobe, temporal lobe, and limbic system, in people with depression compared to healthy people. In addition, people with depression also had abnormal functional connectivity to the default mode network, the central executive network, and the salience network. These findings provide an opportunity to re-understand the biological mechanisms of depression. In the future, magnetic resonance imaging (MRI) may serve as an important auxiliary tool for psychiatrists in the process of early and accurate diagnosis of depression and finding the appropriate treatment target for each patient to optimize clinical response.

Anhedonia

Salience (neuroscience)

Depression

Functional Imaging

10.7717/peerj.8170

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Citations (87)

Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information

Radiology Artificial Intelligence (2024)

Lisong Dai Jihong Lei Fenglong Ma Zheng Sun Haiyan Du

A deep learning model incorporating radiology report–derived textual features outperformed a basic deep learning model trained without textual features in identifying 14 brain diseases and normal brain on MR images while providing interpretable attention maps.

Boosting

10.1148/ryai.230520

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A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study

European Radiology (2024)

Jingxuan Jiang Kai Sheng Minda Li Huilin Zhao Baohui Guan

Nomogram

Neuroradiology

10.1007/s00330-024-10802-8

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Citations (1)