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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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Automated Classification and Segmentation and Feature Extraction from Breast Imaging Data
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Combination of Gabor Filter and Convolutional Neural Network for Suspicious Mass Classification
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THE NECESSITY FOR PARENTS TO WATCH ANIMATED CARTOONS WITH CHILDREN AGED SEVEN TO ELEVEN YEARS
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Intelligent Vision in Healthcare
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Cell Detection with Deep Learning Accelerated by Sparse Kernel
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Skin Cancer Prediction using Deep Learning
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Semi-supervised Cell Detection in Time-Lapse Images Using Temporal Consistency
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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