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.
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 study 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.

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.
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 study 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.

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