ArticleRomanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
Agreement of two pre-trained deep-learning neural networks built with transfer learning with six pathologists on 6000 patches of prostate cancer from Gleason2019 Challenge.
Article in Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Role of Contrast-Enhanced Ultrasonography in Hepatocellular Carcinoma by Using LI-RADS and Ancillary Features: A Single Tertiary Centre Experience.Diagnostics (Basel, Switzerland) · 2021Article
- An Insight into Deficient Mismatch Repair Colorectal Cancer Screening in a Romanian Population-A Bi-Institutional Pilot Study.Medicina (Kaunas, Lithuania) · 2021Article
- Reliable deep learning for coronary artery disease detection: a patient-level, statistically validated MRI study.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Nuclear morphology explained through digital morphometry: differentiating nuclear features across the three histological grades in cutaneous squamous cell carcinoma.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Maternal Lipid Profile as Predictor for Mother and Fetus Outcome-an Artificial Neural Network Approach.Current health sciences journalArticle
- Analysis of the relationship between placental histopathological aspects of preterm and term birth.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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7 authors.
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Abstract
introductionWhile the visual inspection of histopathology images by expert pathologists remains the golden standard method for grading of prostate cancer the quest for developing automated algorithms for the job is set and deep-learning techniques have emerged on top of other approaches.
methodsTwo pre-trained deep-learning networks, obtained with transfer learning from two general purpose classification networks - AlexNet and GoogleNet, originally trained on a proprietary dataset of prostate cancer were used to classify 6000 cropped images from Gleason2019 Challenge.
resultsThe average agreement between the two networks and the six pathologists was found to be substantial for AlexNet and moderate for GoogleNet. When tested against the majority vote of the six pathologists the agreement was perfect and moderate for AlexNet, and GoogleNet, respectively. Despite our expectations, the average inter-pathologist agreement was moderate, while between the two networks it was substantial. Resulted accuracy for AlexNet and GoogleNet when tested against the majority vote as ground truth was of 85.51% and 74.75%, respectively. This result was higher than the score obtained on the dataset that they were trained on, showing their generalization capabilities.
conclusionsBoth the agreement and the accuracy indicate a better performance of AlexNet over GoogleNet, making it suitable for clinical deployment thus could potentially contribute to faster, more accurate and with higher reproducibility prostate cancer diagnosis.
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