Research Summary

Research Subjects

• The discovery of novel disease mechanisms and therapies using Artificial Intelligence
• Precise companion diagnostics system
• Comprehensive analysis of medical data

Research Subjects

• The discovery of novel disease mechanisms and therapies using Artificial Intelligence
• Precise companion diagnostics system
• Comprehensive analysis of medical data

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

• 1. Yamamoto Y, Tsuzuki T, Akatsuka J, Ueki M, Morikawa H, Numata Y, Takahara T, Tsuyuki T, Tsutsumi K, Nakazawa R, Shimizu A, Maeda I, Tsuchiya S, Kanno H, Kondo Y, Fukumoto M, Tamiya G, Ueda N, Kimura G.:
  "Automated acquisition of explainable knowledge from unannotated histopathology images."
  Nat Commun. 10, 5642 (2019).
• 2. Akatsuka J, Yamamoto Y, Sekine T, Numata Y, Morikawa H, Tsutsumi K, Yanagi M, Endo Y, Takeda H, Hayashi T, Ueki M, Tamiya G, Maeda I, Fukumoto M, Shimizu A, Tsuzuki T, Kimura G, Kondo Y.:
  "Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches."
  Biomolecules. 9, 673 (2019).
• 3. Yamamoto Y, Saito A, Tateishi A, Shimojo H, Kanno H, Tsuchiya S, Ito KI, Cosatto E, Graf HP, Moraleda RR, Eils R, and Grabe N.:
  "Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach."
  Sci Rep. 7, 46732 (2017).
• 4. *Yamamoto Y, Offord CP, Kimura G, Kuribayashi S, Takeda H, Tsuchiya S, Shimojo H, Kanno H, Bozic I, Nowak MA, Bajzer ž, and Dingli D.:
  "Tumor and immune cell dynamics explain the PSA bounce after prostate cancer brachytherapy."
  Br J Cancer. 115, 195-202 (2016).
• 5. *Saito A, Numata Y, Hamada T, Horisawa T, Cosatto E, Graf HP, Kuroda M, and Yamamoto Y.:
  "A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix."
  J Pathol Inform. 7, 36 (2016).
• 6. *Yamada M, Saito A, Yamamoto Y, Cosatto E, Kurata A, Nagao T, Tateishi A, and Kuroda M.:
  "Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast."
  J Pathol Inform. 7, 1 (2016).
• 7. *Ogura M, Yamamoto Y, Miyashita H, Kumamoto H, and Fukumoto M.:
  "Quantitative analysis of nuclear shape in oral squamous cell carcinoma is useful for predicting the chemotherapeutic response."
  Med Mol Morphol. 49, 76-82 (2015).
• 8. *Yamamoto Y, Usuda N, Oghiso Y, Kuwahara Y, and Fukumoto M.:
  "The uneven irradiation of a target cell and its dynamic movement can mathematically explain incubation period for the induction of cancer by internally deposited radionuclides."
  Health Phys. 99, 388-393 (2010).
• 9. *Yamamoto Y, Chikawa J, Uegaki Y, Usuda N, Kuwahara Y, and Fukumoto M.:
  "Histological type of Thorotrast-induced liver tumors associated with the translocation of deposited radionuclides."
  Cancer Sci. 101, 336-340 (2010).
• 10. *Yamamoto Y, Usuda N, Takatsuji T, Kuwahara Y, and Fukumoto M.:
  "Long incubation period for the induction of cancer by Thorotrast is attributed to the uneven irradiation of liver cells at the microscopic level."
  Radiat Res. 171, 494-503 (2009).

Recent Research Results

• 

  Mar. 19, 2020
  New clues in AI cancer prognoses

• 

  Dec. 18, 2019
  Artificial intelligence identifies previously unknown features associated with cancer recurrence

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