title: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.
abstract: Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Geert
authors.initials: G
authors.lastname: Litjens
authors.affiliation: Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Clara I
authors.initials: CI
authors.lastname: Sánchez
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Nadya
authors.initials: N
authors.lastname: Timofeeva
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Meyke
authors.initials: M
authors.lastname: Hermsen
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Iris
authors.initials: I
authors.lastname: Nagtegaal
authors.affiliation: Department of Pathology, Amphia Breda Medical Center, The Netherlands.
authors.forename: Iringo
authors.initials: I
authors.lastname: Kovacs
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Christina
authors.initials: C
authors.lastname: Hulsbergen-van de Kaa
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Peter
authors.initials: P
authors.lastname: Bult
authors.affiliation: Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.affiliation: Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Jeroen
authors.initials: J
authors.lastname: van der Laak
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006652:Histological Techniques; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008207:Lymphatic Metastasis / Q000175:diagnosis / Q000473:pathology; D000069550:Machine Learning; D008297:Male; D009367:Neoplasm Staging / Q000379:methods; D016571:Neural Networks, Computer; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D021701:Sentinel Lymph Node Biopsy