Cited by 403AI in cancer detectionCutaneous Melanoma Detection and ManagementImage Retrieval and Classification Techniques

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A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

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Cruz-Roa, Angel Alfonso, Arevalo Ovalle, John Edison, Madabhushi, Anant, González Osorio, Fabio Augusto·2013·Lecture Notes in Computer Science

DOI 10.1007/978-3-642-40763-5_50

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Abstract

This paper presents and evaluates a deep learning architecture for automated basal cell carcinoma cancer detection that integrates (1) image representation learning, (2) image classification and (3) result interpretability. A novel characteristic of this approach is that it extends the deep learning architecture to also include an interpretable layer that highlights the visual patterns that contribute to discriminate between cancerous and normal tissues patterns, working akin to a digital staining which spotlights image regions important for diagnostic decisions. Experimental evaluation was performed on set of 1,417 images from 308 regions of interest of skin histopathology slides, where the presence of absence of basal cell carcinoma needs to be determined. Different image representation strategies, including bag of features (BOF), canonical (discrete cosine transform (DCT) and Haar-based wavelet transform (Haar)) and proposed learned-from-data representations, were evaluated for comparison. Experimental results show that the representation learned from a large histology image data set has the best overall performance (89.4% in F-measure and 91.4% in balanced accuracy), which represents an improvement of around 7% over canonical representations and 3% over the best equivalent BOF representation.

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Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695
Self-taught learning
Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,534
ImageNet classification with deep convolutional neural networks
Krizhevsky (2012) · Medical Entomology and Zoology · Cited by 799
Building high-level features using large scale unsupervised learning
Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., Le, Q. V., & Ng, A. Y. (2012) · International Conference on Machine Learning · Cited by 646
Histology image analysis for carcinoma detection and grading
He, L., Long, L. R., Antani, S., & Thoma, G. R. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.12.007 · Cited by 404
Building high-level features using large scale unsupervised learningPreprint
Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., & Ng, A. Y. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.6209 · Cited by 341
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
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Visual pattern mining in histology image collections using bag of features
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Show all 15 references

Research works printed but not linked: 1

Montavon, G.: A machine learning approach to classification of low resolution histological samples. Master’s thesis (2009)

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Authors: Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A.
Affiliations: Case Western Reserve University (US)
ORCID: Madabhushi, Anant
Venue: Lecture Notes in Computer Science
Publisher: Springer Berlin Heidelberg
Published: 2013
Volume: 16
Issue: Pt 2
Pages: 403-410
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-642-40763-5_50
OpenAlex: W1932469787
Cited by: 403
References: 15
Citations by year: 2026: 4 · 2025: 17 · 2024: 21 · 2023: 35 · 2022: 46 · 2021: 55 · 2020: 33 · 2019: 62 · 2018: 42 · 2017: 41 · 2016: 24 · 2015: 12 · 2014: 8 · 2013: 2
FWCI: 9.90
Type: conference paper
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Lecture notes in computer science (publishedVersion)
PubMed (publishedVersion)
Funders: Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS) (528–2011)
Universidad Nacional de Colombia
National Institutes of Health (5r03ca143991-02)
National Cancer Institute (R03CA143991-01, R01CA140772, R01CA136535)
National Institute of Biomedical Imaging and Bioengineering (R43EB015199)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords: basal cell carcinoma, skin cancer detection, histopathological image classification, image representation learning, visual interpretability, saliency maps, bag of visual words, discrete cosine transform, Haar wavelet transform, skin histology
MeSH terms: Algorithms, Artificial Intelligence, Biopsy, Basal Cell Carcinoma, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Pattern Recognition, Automated, Sensitivity and Specificity, Skin Neoplasms, Reproducibility of Results

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A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

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