Machine Learning Methods for Histopathological Image Analysis
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Summary
In this mini-review, the application of digital pathological image analysis using machine learning algorithms is introduced, some problems specific to such analysis are addressed, and possible solutions are proposed.
- Type
- review
- Published
- 2017-09-04
- Cited by
- 868
- References
- 111
- Access
- Open access
- OpenAlex
- https://openalex.org/W2751723768
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3489958
Keywords
Digital image analysis, Computer science, Artificial intelligence, Digital image, Digital pathology
References
- Scalable Histopathological Image Analysis via Active Learning
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- The Cancer Genome Atlas Pan-Cancer Analysis Project
- The Genotype-Tissue Expression (GTEx) project
- Machine learning approaches to analyze histological images of tissues from radical prostatectomies
- Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces
- Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images
- Engaging and Mobilizing Community Members to Prevent Obesity Among Adolescents
- Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade
- Digital pathology image analysis: opportunities and challenges
- Content-based histopathology image retrieval using a kernel-based semantic annotation framework
- An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors
- The Stanford Tissue Microarray Database
- Digital images and the future of digital pathology
- Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images
- An active learning based classification strategy for the minority class problem: application to histopathology annotation
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- Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies
- Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis.
- Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
- Finding a Needle in the Haystack: Attention-Based Classification of High Resolution Microscopy Images
- Diagnosis of mesothelioma with deep learning
- A comparative analysis of sensitivity of convolutional neural networks for histopathology image classification in breast cancer
- Artificial Intelligence in Pathology
- Embedding of Genes Using Cancer Gene Expression Data: Biological Relevance and Potential Application on Biomarker Discovery
- Feature extraction and Cluster analysis of Pancreatic Pathological Image Based on Unsupervised Convolutional Neural Network
- Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
- Artificial Intelligence for the Otolaryngologist: A State of the Art Review
- Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images
- Automated acquisition of knowledge beyond pathologists
- Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques
- Impact of pre-analytic variables on deep learning accuracy in histopathology
- Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology
- Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association
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