ArticleRomanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.Acta oncologica (Stockholm, Sweden) · 2026Article
- Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Article
- Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN.Scientific reports · 2024Article
- Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.Scientific reports · 2023Article
- Review
- Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma.Diagnostics (Basel, Switzerland) · 2022Article
- 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
- Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Immunohistochemical expression of p53, Ki67, α-SMA, CD44 and CD31 in different histological subtypes of basal cell carcinoma.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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4 authors.
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Abstract
Establishing basal cell carcinoma (BCC) subtype is sometimes challenging for pathologists. Deep-learning (DL) algorithms are an emerging approach in image classification due to their performance, accompanied by a new concept - transfer learning, which implies replacing the final layers of a trained network and retraining it for a new task, while keeping the weights from the imported layers. A DL convolution-based software, capable of classifying 10 subtypes of BCC, was designed. Transfer learning from three general-purpose image classification networks (AlexNet, GoogLeNet, and ResNet-18) was used. Three pathologists independently labeled 2249 patches. Ninety percent of data was used for training and 10% for testing on 100 independent training sequences. Each of the resulted networks independently labeled the whole dataset. Mean and standard deviation (SD) accuracy (ACC) [%]∕sensitivity (SN) [%]∕specificity (SP) [%]∕area under the curve (AUC) for all the networks was 82.53±2.63∕72.52±3.63∕97.94±0.3/0.99. The software was validated on another 50-image dataset, and its results are comparable with the result of three pathologists in terms of agreement. All networks had similar classification accuracies, which demonstrated that they reached a maximum classification rate on the dataset. The software shows promising results, and with further development can be successfully used on histological images, assisting pathologists' diagnosis and teaching.
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