ArticleRomanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie2020
Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks.
Article in Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Article
- Assessment of interobserver variability in Gleason grading for prostate carcinoma.Northern clinics of Istanbul · 2025Article
- A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.Frontiers in oncology · 2023Article
- Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey.Sensors (Basel, Switzerland) · 2021Review
- 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
- 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.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Leveraging artificial intelligence for cardiovascular risk: a primary care perspective.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
- 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
- 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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8 authors.
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Abstract
Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for GoogleNet derivate network was of 60.9±7.4. The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset. Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.
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