ArticleBMC bioinformatics2017
Parallel multiple instance learning for extremely large histopathology image analysis.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images.iScience · 2023Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer.IEEE journal of translational engineering in health and medicine · 2022Article
- Deep multiple-instance learning for abnormal cell detection in cervical histopathology images.Computers in biology and medicine · 2021Article
- Development and operation of a digital platform for sharing pathology image data.BMC medical informatics and decision making · 2021Article
- Deep learning in digital pathology image analysis: a survey.Frontiers of medicine · 2020Review
- Article
- HLMethy: a machine learning-based model to identify the hidden labels of mPlant molecular biology · 2019Article
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
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Authors and funding
8 authors.
Funding
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Abstract
backgroundHistopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power.
resultsIn this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications.
conclusionsThe framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.
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