ArticleBioinformatics (Oxford, England)2022
Deep learning for survival analysis in breast cancer with whole slide image data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Multi-scale Multimodal Representation for Enhanced Survival Prediction in Computational Pathology.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Automatic ovarian follicle detection using object detection models.Scientific reports · 2024Article
- Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.NPJ digital medicine · 2024Article
- Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Topology-Guided Multi-Class Cell Context Generation for Digital Pathology.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Article
- Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images.Scientific reports · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
motivationWhole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer mechanisms. Such analyses are challenging to execute because of the sizes and complexity of whole slide image data and relatively limited volume of training data for machine learning methods.
resultsWe propose and experimentally evaluate a multi-resolution deep learning method for breast cancer survival analysis. The proposed method integrates image data at multiple resolutions and tumor, lymphocyte and nuclear segmentation results from deep learning models. Our results show that this approach can significantly improve the deep learning model performance compared to using only the original image data. The proposed approach achieves a c-index value of 0.706 compared to a c-index value of 0.551 from an approach that uses only color image data at the highest image resolution. Furthermore, when clinical features (sex, age and cancer stage) are combined with image data, the proposed approach achieves a c-index of 0.773. AVAILABILITY AND IMPLEMENTATION: https://github.com/SBU-BMI/deep_survival_analysis.
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