ArticleFrontiers in oncology2021
Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Evaluation of the Usefulness of Machine Learning and Artificial Intelligence on Hyperspectral Images in the Diagnosis of Myelodysplastic Syndrome.Journal of biophotonics · 2026Article
- A comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?Cell division · 2025Review
- Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.The journal of pathology. Clinical research · 2025Article
- IDH-mutant glioma risk stratification via whole slide images: Identifying pathological feature associations.iScience · 2025Article
- Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.European radiology · 2024Article
- Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.BMC gastroenterology · 2024Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence.Cancers · 2024Review
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer.PloS one · 2024Article
- Information maximization-based clustering of histopathology images using deep learning.PLOS digital health · 2023Article
- ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification.Computer methods and programs in biomedicine · 2023Article
- Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma.Nature communications · 2023Article
Corrections and comments
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Authors and funding
12 authors.
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Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.
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