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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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“[61], [132]–[137], segmentation [46], [138]–[142], recognition [143]–[146], and brain behavior ‘decoding’ [50].”
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“The authors in [10] employed sparse AEs to learn an unsupervised representation that feeds a softmax classifier over this representation identifying the image regions that are most relevant for the basal cell carcinoma cancer detection.”
Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
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“highlight the use of deep learning for several tasks [83], [84].”
BackgroundUsing Double Convolution Neural Network for Lung Cancer Stage Detection
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“For additional future work, similar to Cruz-Roa and Arevalo Ovalle in [29], who used RGB (color) images to highlight the area of malignant cells, we plan on modifying the DNN to show us where (the location) on the CT image it has detected a cancer.”
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“Deep learning has been used to diagnose basal cell carcinoma from histopathology images (Cruz-Roa et al. 2013) and for differentiating medulloblastoma (Cruz-Roa et al.”
MethodAnalysis of Histopathology Images
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“…A second group of researchers focused on patch level analysis (e.g., small regions) Bhagavatula et al. (2010); Kong et al. (2010); Han et al. (2011); Cruz-Roa et al. (2013) for tumor representation, among which, Cruz-Roa et al. (2013) utilized deep learning for the detection of basalcell carcinoma.”
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“However, their autoencoder contains only one layer for the feature representation 39 .”
Background
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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