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Congress: ECR24
Poster Number: C-14045
Type: EPOS Radiologist (scientific)
Authorblock: S. Kasai1, H. Tamori2, C. Kai1; 1Niigata city/JP, 2Yokohama city/JP
Disclosures:
Satoshi Kasai: Research/Grant Support: Konica Minolta Inc. Consultant: Konica Minolta Inc.
Hideaki Tamori: Nothing to disclose
Chiharu Kai: Nothing to disclose
Keywords: Breast, Computer applications, Oncology, CAD, Digital radiography, Mammography, CAD, Computer Applications-Detection, diagnosis, Cancer
References

1. Robertson S, et al: Re-testing of predictive biomarkers on surgical breast cancer specimens is clinically relevant. Breast Cancer Res Treat, 174(3), 795-805, 2019.

2. Chen J, et al: Comparison of Core Needle Biopsy and Excision Specimens for the Accurate Evaluation of Breast Cancer Molecular Marker: a Report of 1003 Cases. Pathol Oncol Res, 23(4), 769-775, 2017.

3. Nakamura S, et al: Subtype Classification of Breast Cancer based on MRI Image. The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, 2019

4. Ma W, et al: Breast Cancer Molecular  Subtypes Prediction by Mammographic Radiomics Features. Acad Radiol, 26(2), 196-201, 2018.

5. Akiba, et al: Optuna: A Next-generation Hyperparameter Optimization Framework: https://arxiv.org/abs/1907.10902
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