1Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan2NTUST Center of Computer Vision and Medical Imaging, Taipei, Taiwan3Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan4Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA5Department of Biostatistics, University of Texas, MD Anderson Cancer Center, TX, USA6Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan7Department of Physics, The University of Tokyo, Tokyo, Japan8Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan
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Motivation:The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients' background information, histopathological and immunohistochemical tissue images.Results:A novel framework for objective evaluation of automatic patient diagnosis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2017— A Grand Challenge for Tissue Microarray Analysis in Thyroid Cancer Diagnosis. Here, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the data repository of tissue microarrays; the creation of the clinical diagnosis classification data repository of thyroid cancer; and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, three automatic methods for predictions of the five clinical outcomes have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic patient diagnosis is still a challenging and unsolved problem.Availability and implementation:The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/cvmi/ISBI2017/).Contact:email@example.comSupplementary information:Supplementary data are available at Bioinformatics online.