ArticleComputational and mathematical methods in medicine2019
Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning.
Article in Computational and mathematical methods in medicine, 2019. 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.
- AI-based tumor cellularity assessment in digital pathology: A review of methods, datasets, and clinical translation.Journal of pathology informatics · 2026Review
- A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.PloS one · 2024Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.Patterns (New York, N.Y.) · 2022Article
- Tissue management in precision medicine: What the pathologist needs to know in the molecular era.Frontiers in molecular biosciences · 2022Review
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior.Cancers · 2021Article
- Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images.Journal of imaging · 2020Article
- Surgical spectral imaging.Medical image analysis · 2020Review
- Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
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Authors and funding
4 authors.
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Abstract
Residual cancer burden (RCB) has been proposed to measure the postneoadjuvant breast cancer response. In the workflow of RCB assessment, estimation of cancer cellularity is a critical task, which is conventionally achieved by manually reviewing the hematoxylin and eosin- (H&E-) stained microscopic slides of cancer sections. In this work, we develop an automatic and direct method to estimate cellularity from histopathological image patches using deep feature representation, tree boosting, and support vector machine (SVM), avoiding the segmentation and classification of nuclei. Using a training set of 2394 patches and a test set of 185 patches, the estimations by our method show strong correlation to those by the human pathologists in terms of intraclass correlation (ICC) (0.94 with 95% CI of (0.93, 0.96)), Kendall's tau (0.83 with 95% CI of (0.79, 0.86)), and the prediction probability (0.93 with 95% CI of (0.91, 0.94)), compared to two other methods (ICC of 0.74 with 95% CI of (0.70, 0.77) and 0.83 with 95% CI of (0.79, 0.86)). Our method improves the accuracy and does not rely on annotations of individual nucleus.
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