The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection
Open accessAbstract
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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“Evaluated on approximately 1400 image patches drawn from annotated tissue regions, the model achieved a balanced accuracy of 91.4%, outperforming conventional feature-based approaches [22].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [22]
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“Several algorithms incorporating artificial intelligence (AI) have been developed to help interpret both clinical11,12,13,14,15 and dermoscopic16,17,18,19,20,21,22,23 images for a variety of skin conditions”
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Cites
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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
Cruz-Roa, A., Caicedo, J. C., & González, F. A. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.04.010 · Cited by 133
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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Items 401–403 of 403
dos Santos, G. C., Silva, H. V. L. B., Araújo, A. L. D., Nakamura, T. C. R., Santos, M. G. S., Claret, A. F., de Figueiredo, F. A. P., Sousa‐Neto, S. S., Giraldo‐Roldán, D., de Freitas, L. M. R., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9596946/v1
dos Santos, G. C., Silva, H. V. L. B., Araújo, A. L. D., Nakamura, T. C. R., Santos, M., Claret, A. F., de Figueiredo, F. A. P., Sousa‐Neto, S. S., Giraldo‐Roldán, D., de Freitas, L. M. R., et al. (2026) · Head and Neck Pathology · doi:10.1007/s12105-026-01939-7
“CNN-based approaches have demonstrated feasibility in soft tissue tumor recognition and H&N pathology applications [ 8–10 ].”
Metadata
- 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
- 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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