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  1. Article ; Online: RICORD: A Precedent for Open AI in COVID-19 Image Analytics.

    Bai, Harrison X / Thomasian, Nicole M

    Radiology

    2021  Volume 299, Issue 1, Page(s) E219–E220

    MeSH term(s) Artificial Intelligence ; COVID-19 ; Data Management ; Humans ; Radiology ; SARS-CoV-2
    Language English
    Publishing date 2021-01-05
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2020204214
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  2. Article: Editorial: Advances of radiomics and artificial intelligence in the management of patients with central nervous system tumors.

    Chen, Ziyan / Zhang, Helen / Zhang, Paul J Z / Bai, Harrison X / Li, Xuejun

    Frontiers in oncology

    2023  Volume 13, Page(s) 1081301

    Language English
    Publishing date 2023-01-19
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2023.1081301
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  3. Article ; Online: AC-E Network: Attentive Context-Enhanced Network for Liver Segmentation.

    Li, Yang / Zou, Beiji / Dai, Peishan / Liao, Miao / Bai, Harrison X / Jiao, Zhicheng

    IEEE journal of biomedical and health informatics

    2023  Volume 27, Issue 8, Page(s) 4052–4061

    Abstract: Segmentation of liver from CT scans is essential in computer-aided liver disease diagnosis and treatment. However, the 2DCNN ignores the 3D context, and the 3DCNN suffers from numerous learnable parameters and high computational cost. In order to ... ...

    Abstract Segmentation of liver from CT scans is essential in computer-aided liver disease diagnosis and treatment. However, the 2DCNN ignores the 3D context, and the 3DCNN suffers from numerous learnable parameters and high computational cost. In order to overcome this limitation, we propose an Attentive Context-Enhanced Network (AC-E Network) consisting of 1) an attentive context encoding module (ACEM) that can be integrated into the 2D backbone to extract 3D context without a sharp increase in the number of learnable parameters; 2) a dual segmentation branch including complemental loss making the network attend to both the liver region and boundary so that getting the segmented liver surface with high accuracy. Extensive experiments on the LiTS and the 3D-IRCADb datasets demonstrate that our method outperforms existing approaches and is competitive to the state-of-the-art 2D-3D hybrid method on the equilibrium of the segmentation precision and the number of model parameters.
    MeSH term(s) Humans ; Abdomen ; Liver Neoplasms ; Tomography, X-Ray Computed/methods ; Diagnosis, Computer-Assisted ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2023-08-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2023.3278079
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  4. Article ; Online: Facial De-identification of Head CT Scans.

    Collins, Scott A / Wu, Jing / Bai, Harrison X

    Radiology

    2020  Volume 296, Issue 1, Page(s) 22

    MeSH term(s) Data Anonymization ; Face/diagnostic imaging ; Head/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2020-04-07
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2020192617
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  5. Article ; Online: Beyond antioxidation: Harnessing the CeO

    Feng, Cai / Xiong, Zongling / Sun, Xianting / Zhou, Hao / Wang, Tianming / Wang, Ying / Bai, Harrison X / Lei, Peng / Liao, Weihua

    Biomaterials

    2023  Volume 299, Page(s) 122164

    Abstract: It is a challenging task to develop a contrast agent that not only provides excellent image contrast but also protects impaired kidneys from oxidative-related stress during angiography. Clinically approved iodinated CT contrast media are associated with ... ...

    Abstract It is a challenging task to develop a contrast agent that not only provides excellent image contrast but also protects impaired kidneys from oxidative-related stress during angiography. Clinically approved iodinated CT contrast media are associated with potential renal toxicity, making it necessary to develop a renoprotective contrast agent. Here, we develop a CeO
    MeSH term(s) Computed Tomography Angiography ; Contrast Media ; Antioxidants ; Nanoparticles ; Kidney/diagnostic imaging ; Cerium
    Chemical Substances Contrast Media ; Antioxidants ; Cerium (30K4522N6T)
    Language English
    Publishing date 2023-05-16
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 603079-8
    ISSN 1878-5905 ; 0142-9612
    ISSN (online) 1878-5905
    ISSN 0142-9612
    DOI 10.1016/j.biomaterials.2023.122164
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  6. Article ; Online: Machine intelligence in non-invasive endocrine cancer diagnostics.

    Thomasian, Nicole M / Kamel, Ihab R / Bai, Harrison X

    Nature reviews. Endocrinology

    2021  Volume 18, Issue 2, Page(s) 81–95

    Abstract: Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data ... ...

    Abstract Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Delivery of Health Care ; Humans ; Machine Learning ; Neoplasms
    Language English
    Publishing date 2021-11-09
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2489381-X
    ISSN 1759-5037 ; 1759-5029
    ISSN (online) 1759-5037
    ISSN 1759-5029
    DOI 10.1038/s41574-021-00543-9
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  7. Article ; Online: Discriminative error prediction network for semi-supervised colon gland segmentation.

    Zhang, Zhenxi / Tian, Chunna / Bai, Harrison X / Jiao, Zhicheng / Tian, Xilan

    Medical image analysis

    2022  Volume 79, Page(s) 102458

    Abstract: Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation ... ...

    Abstract Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation directly affects the accuracy on the test set and the subsequent self-training with the error-corrected pseudo labels. In this paper, we propose a novel label rectification method based on error correction, namely ECLR, which can be directly added after the fully-supervised segmentation framework. Moreover, it can be used to guide the semi-supervised learning (SSL) process, constituting an error correction guided SSL framework, called ECGSSL. Specifically, we analyze the types and causes of segmentation error, and divide it into intra-class error and inter-class error caused by intra-class inconsistency and inter-class similarity problems in segmentation, respectively. Further, we propose a collaborative multi-task discriminative error prediction network (DEP-Net) to highlight two error types. For better training of DEP-Net, we propose specific mask degradation methods representing typical segmentation errors. Under the fully-supervised regime, the pre-trained DEP-Net is used to directly rectify the initial segmentation results of the test set. While, under the semi-supervised regime, a dual error correction method is proposed for unlabeled data to obtain more reliable network re-training. Our method is easy to apply to different segmentation models. Extensive experiments on gland segmentation verify that ECLR yields substantial improvements based on initial segmentation predictions. ECGSSL shows consistent improvements over a supervised baseline learned only from labeled data and achieves competitive performance compared with other popular semi-supervised methods.
    MeSH term(s) Colon ; Humans ; Supervised Machine Learning
    Language English
    Publishing date 2022-04-22
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2022.102458
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  8. Article ; Online: An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

    Li, Yang / Imami, Maliha R / Zhao, Linmei / Amindarolzarbi, Alireza / Mena, Esther / Leal, Jeffrey / Chen, Junyu / Gafita, Andrei / Voter, Andrew F / Li, Xin / Du, Yong / Zhu, Chengzhang / Choyke, Peter L / Zou, Beiji / Jiao, Zhicheng / Rowe, Steven P / Pomper, Martin G / Bai, Harrison X

    Journal of imaging informatics in medicine

    2024  

    Abstract: Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We ... ...

    Abstract Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [
    Language English
    Publishing date 2024-04-08
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2948-2933
    ISSN (online) 2948-2933
    DOI 10.1007/s10278-024-01104-y
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  9. Article ; Online: Artificial intelligence for medical image analysis in epilepsy.

    Sollee, John / Tang, Lei / Igiraneza, Aime Bienfait / Xiao, Bo / Bai, Harrison X / Yang, Li

    Epilepsy research

    2022  Volume 182, Page(s) 106861

    Abstract: Given improvements in computing power, artificial intelligence (AI) with deep learning has emerged as the state-of-the art method for the analysis of medical imaging data and will increasingly be used in the clinical setting. Recent work in epilepsy ... ...

    Abstract Given improvements in computing power, artificial intelligence (AI) with deep learning has emerged as the state-of-the art method for the analysis of medical imaging data and will increasingly be used in the clinical setting. Recent work in epilepsy research has aimed to use AI methods to improve diagnosis, prognosis, and treatment, with the ultimate goal of developing highly accurate and reliable tools to aid clinical decision making. Here, we review how researchers are currently using AI methods in the analysis of neuroimaging data in epilepsy, focusing on challenges unique to each imaging modality with an emphasis on clinical significance. We further provide critical analyses of existing techniques and recommend areas for future work. We call for: (1) a multimodal approach that leverages the strengths of different modalities while compensating for their individual weaknesses, and (2) widespread implementation of generalizability testing of proposed models, a needed step before their introduction into clinical workflows. To achieve both goals, more collaborations among research groups and institutions in this field will be required.
    MeSH term(s) Artificial Intelligence ; Clinical Decision-Making ; Epilepsy/diagnostic imaging ; Humans
    Language English
    Publishing date 2022-01-19
    Publishing country Netherlands
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 632939-1
    ISSN 1872-6844 ; 0920-1211
    ISSN (online) 1872-6844
    ISSN 0920-1211
    DOI 10.1016/j.eplepsyres.2022.106861
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  10. Article ; Online: Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.

    Lancaster, Andrew C / Cardin, Mitchell E / Nguyen, Jan A / Mehta, Tej I / Oncel, Dilek / Bai, Harrison X / Cohen, Keira A / Lin, Cheng Ting

    Journal of thoracic imaging

    2023  Volume 39, Issue 3, Page(s) 194–199

    Abstract: Purpose: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT).: Materials and methods: We ...

    Abstract Purpose: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT).
    Materials and methods: We collected a data set of 650 nodules (316 acute and 334 chronic) from the CT scans of 110 patients with NTM-LD. The data set was divided into training, validation, and test sets in a ratio of 4:1:1. Bounding boxes were used to crop the 2D CT images down to the area of interest. A DCNN model was built using 11 convolutional layers and trained on these images. The performance of the model was evaluated on the hold-out test set and compared with that of 3 radiologists who independently reviewed the images.
    Results: The DCNN model achieved an area under the receiver operating characteristic curve of 0.806 for differentiating acute and chronic NTM-LD nodules, corresponding to sensitivity, specificity, and accuracy of 76%, 68%, and 72%, respectively. The performance of the model was comparable to that of the 3 radiologists, who had area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of 0.693 to 0.771, 61% to 82%, 59% to 73%, and 60% to 73%, respectively.
    Conclusions: This study demonstrated the feasibility of using a DCNN model for the classification of the activity of NTM-LD nodules on chest CT. The model performance was comparable to that of radiologists. This approach can potentially and efficiently improve the diagnosis and management of NTM-LD.
    MeSH term(s) Humans ; Deep Learning ; Neural Networks, Computer ; Pneumonia ; Tomography, X-Ray Computed/methods ; Radiographic Image Interpretation, Computer-Assisted/methods ; Retrospective Studies ; Lung Neoplasms/diagnostic imaging
    Language English
    Publishing date 2023-11-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 632900-7
    ISSN 1536-0237 ; 0883-5993
    ISSN (online) 1536-0237
    ISSN 0883-5993
    DOI 10.1097/RTI.0000000000000745
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