ArticleJournal of healthcare engineering2018
Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020Pooled it
- Deep learning in gastric tissue diseases: a systematic review.BMJ open gastroenterology · 2020Pooled it
- AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.Scientific reports · 2026Article
- Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning.Cancers · 2023Article
- Review
- Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer.Bioengineering (Basel, Switzerland) · 2022Article
- Comparison of Fine-Tuned Deep Convolutional Neural Networks for the Automated Classification of Lung Cancer Cytology Images with Integration of Additional Classifiers.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network.Scientific reports · 2022Article
- Identification of gastric cancer with convolutional neural networks: a systematic review.Multimedia tools and applications · 2022Article
- Improving feature extraction from histopathological images through a fine-tuning ImageNet model.Journal of pathology informatics · 2022Article
- Primary Investigation of Deep Learning Models for Japanese "Group Classification" of Whole-Slide Images of Gastric Endoscopic Biopsy.Computational and mathematical methods in medicine · 2022Article
- GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion.Frontiers in oncology · 2022Article
- Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured health data.Health information science and systems · 2021Article
- Review
- Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology.World journal of gastroenterology · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Artificial intelligence in gastroenterology and hepatology: Status and challenges.World journal of gastroenterology · 2021Review
- Review
- Artificial intelligence in gastric cancer: a translational narrative review.Annals of translational medicine · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning using convolutional neural networks (CNNs) is a distinguished tool for many image classification tasks. Due to its outstanding robustness and generalization, it is also expected to play a key role to facilitate advanced computer-aided diagnosis (CAD) for pathology images. However, the shortage of well-annotated pathology image data for training deep neural networks has become a major issue at present because of the high-cost annotation upon pathologist's professional observation. Faced with this problem, transfer learning techniques are generally used to reinforcing the capacity of deep neural networks. In order to further boost the performance of the state-of-the-art deep neural networks and alleviate insufficiency of well-annotated data, this paper presents a novel stepwise fine-tuning-based deep learning scheme for gastric pathology image classification and establishes a new type of target-correlative intermediate datasets. Our proposed scheme is deemed capable of making the deep neural network imitating the pathologist's perception manner and of acquiring pathology-related knowledge in advance, but with very limited extra cost in data annotation. The experiments are conducted with both well-annotated gastric pathology data and the proposed target-correlative intermediate data on several state-of-the-art deep neural networks. The results congruously demonstrate the feasibility and superiority of our proposed scheme for boosting the classification performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.