LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Jiao, Yiping; van der Laak, Jeroen; Albarqouni, Shadi; Li, Zhang; Tan, Tao; Bhalerao, Abhir; Cheng, Shenghua; Ma, Jiabo; Pocock, Johnathan; Pluim, Josien P.W.; Koohbanani, Navid Alemi; Bashir, Raja Muhammad Saad; Raza, Shan E Ahmed; Liu, Sibo; Graham, Simon; Wetstein, Suzanne; Khurram, Syed Ali; Liu, Xiuli; Rajpoot, Nasir; Veta, Mitko; Ciompi, Francesco

doi:10.1109/jbhi.2023.3327489

IEEE J. Biomed. Health Inform.

2024

DOI: 10.1109/jbhi.2023.3327489

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LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Yiping Jiao,

Jeroen van der Laak,

Shadi Albarqouni

et al.

Abstract: We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe … Show more

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Cited by 3 publications

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“…The first paper by Jiao et al [4] introduces LYSTO, the Lymphocyte Assessment Hackathon dataset. This dataset is made up of multi-center and multi-organ pathological images that will serve as a reference for future computational pathology methods.…”

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confidence: 99%

Guest Editorial Artificial Intelligence-Driven Biomedical Imaging Systems for Precision Diagnostic Applications

Kumar,

Singh,

Damasevicius

2024

IEEE J. Biomed. Health Inform.

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mentioning

confidence: 99%

Guest Editorial Artificial Intelligence-Driven Biomedical Imaging Systems for Precision Diagnostic Applications

Kumar,

Singh,

Damasevicius

2024

IEEE J. Biomed. Health Inform.

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Revolutionizing healthcare: a comparative insight into deep learning’s role in medical imaging

Prasad,

Verma,

Bhattacharya

et al. 2024

Sci Rep

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Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the model computational constraints, Cloud Computing (CC) is integrated to operate with the DL models. Recent articles on DL-based MRI have not discussed datasets specific to different diseases, which makes it difficult to build the specific DL model. Thus, the article systematically explores a tutorial approach, where we first discuss a classification taxonomy of medical imaging datasets. Next, we present a case-study on AD MRI classification using the DL methods. We analyse three distinct models-Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach-for classification and predictive outcomes. In addition, we designed a novel framework that offers insight into how various layers interact with the dataset. Our architecture comprises an input layer, a cloud-based layer responsible for preprocessing and model execution, and a diagnostic layer that issues alerts after successful classification and prediction. According to our simulations, CNN outperformed other models with a test accuracy of 99.285%, followed by VGG-16 with 85.113%, while the ensemble model lagged with a disappointing test accuracy of 79.192%. Our cloud Computing framework serves as an efficient mechanism for medical image processing while safeguarding patient confidentiality and data privacy.

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Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Mi,

Sivagnanam,

et al. 2024

Briefings in Bioinformatics

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Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

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LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Cited by 3 publications

References 47 publications

Guest Editorial Artificial Intelligence-Driven Biomedical Imaging Systems for Precision Diagnostic Applications

Guest Editorial Artificial Intelligence-Driven Biomedical Imaging Systems for Precision Diagnostic Applications

Revolutionizing healthcare: a comparative insight into deep learning’s role in medical imaging

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

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