A Boosting Cascade for Automated Detection of Prostate Cancer from Digitized Histology
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Summary
A CAD system to assist pathologists by automatically detecting prostate cancer from digitized images of prostate histological specimens is presented and the method is robust to choice of training samples, and the multi-scale cascaded approach results in significant savings in computational time.
- Type
- article
- Published
- 2006-10-01
- Cited by
- 134
- References
- 10
- Access
- Open access
- OpenAlex
- https://openalex.org/W48715220
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:533961
Keywords
Computer science, CAD, Artificial intelligence, Computer-aided diagnosis, Segmentation
References
- Prostate biopsy: indications and technique.
- Evaluation of prostate tumor grades by content-based image retrieval
- Automated prostate cancer diagnosis and Gleason grading of tissue microarrays
- Textural Features for Image Classification
- Pattern classification and scene analysis
- Pattern classification and scene analysis
- IMAGE DATA COMPRESSION WITH THE LAPLACIAN PYRAMID
- Texture Features for Browsing and Retrieval of Image Data
- Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI
- Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa
- Rapid object detection using a boosted cascade of simple features
- Experiments with a New Boosting Algorithm
- Experiments with a new boosting algorithm
- Pattern Classification and Scene Analysis.
Cited by
- Smart atlas for endomicroscopy diagnosis support: a clinical application of content-based image retrieval. (Atlas intelligent pour guider le diagnostic en endomicroscopie : une application clinique de la reconnaissance d'images par le contenu)
- Analyse statistique de populations pour l'interprétation d'images histologiques. (Statistical analysis of populations for histological images interpretation)
- Methods for Optimal Model Fitting and Sensor Calibration
- Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays
- Computerized detection, segmentation and classification of digital pathology: case study in prostate cancer
- Influence of Texture and Colour in Breast TMA Classification
- Computer assisted detection of regions of interest in histopathology using a hybrid supervised and unsupervised approach
- Histology image analysis for carcinoma detection and grading
- Digital pathology image analysis: opportunities and challenges
- Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation
- Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis
- Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification
- Probabilistic pairwise Markov models: application to prostate cancer detection
- Biologically-driven cell-graphs for breast tissue grading
- A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images
- Integration of Architectural and Cytologic Driven Image Algorithms for Prostate Adenocarcinoma Identification
- An unsupervised feature learning framework for basal cell carcinoma image analysis
- A smart atlas for endomicroscopy using automated video retrieval
- High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts
- Multi-scale lacunarity as an alternative to quantify and diagnose the behavior of prostate cancer
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