ArticleAnnals of biomedical engineering2018
Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks.
Article in Annals of biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Role of artificial intelligence in digital pathology for gynecological cancers.Computational and structural biotechnology journal · 2024Review
- Artificial intelligence in ovarian cancer histopathology: a systematic review.NPJ precision oncology · 2023Review
- DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.Journal of digital imaging · 2023Article
- Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.The Analyst · 2023Article
- Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning.Journal of assisted reproduction and genetics · 2023Article
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.Scientific reports · 2023Article
- Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer.International journal of molecular sciences · 2023Article
- MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images.Health information science and systems · 2022Article
- A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.Bioengineering (Basel, Switzerland) · 2022Article
- Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head.Diagnostics (Basel, Switzerland) · 2022Article
- A deep learning-based automatic staging method for early endometrial cancer on MRI images.Frontiers in physiology · 2022Article
- Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm.PloS one · 2022Article
- Weakly-supervised deep learning for ultrasound diagnosis of breast cancer.Scientific reports · 2021Article
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- Low Tumor-to-Stroma Ratio Reflects Protective Role of Stroma against Prostate Cancer Progression.Journal of personalized medicine · 2021Article
- Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.Applied immunohistochemistry & molecular morphology : AIMM · 2021Article
- COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.Scientific reports · 2021Article
- Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification.Journal of imaging · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor epithelium and stroma are required to elevate the prognostic significance of the TSR. As a variant of deep learning techniques, transfer learning leverages nature-images features learned by deep convolutional neural networks (CNNs) to relieve the requirement of deep CNNs for immense sample size when handling biomedical classification problems. Herein we studied different transfer learning strategies for accurately distinguishing epithelial and stromal regions of H&E-stained histological images acquired from either breast or ovarian cancer tissue. We compared the performance of important deep CNNs as either a feature extractor or as an architecture for fine-tuning with target images. Moreover, we addressed the current contradictory issue about whether the higher-level features would generalize worse than lower-level ones because they are more specific to the source-image domain. Under our experimental setting, the transfer learning approach achieved an accuracy of 90.2 (vs. 91.1 for fine tuning) with GoogLeNet, suggesting the feasibility of using it in assisting pathology-based binary classification problems. Our results also show that the superiority of the lower-level or the higher-level features over the other ones was determined by the architecture of deep CNNs.
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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.