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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Method
Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images
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“Recently, more and more scholars apply the method of deep learning to pathological image analysis, among which convolution neural network model is the most widely used [10]-[13].”
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Multiclass Breast Cancer Classification Using Convolutional Neural Network
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“Some other applications of deep-learning for analyzing microscopic images include detecting metastatic breast cancer [12], finding mitotically active cells [13], identifying basal-cell carcinoma [14], and grading gliomas [15].”
Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers
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“[9] put forward a kind of deep learning method that integrated image representations, basal-cell carcinoma cancer detection and visual interpretation for the skin histopathology images.”
Quaternion Grassmann average network for learning representation of histopathological image
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“Similarly, the SSAE has been applied on cell detection in histopathological image analysis, such as identification of prostate and breast cancer [43][44][45].”
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Method
Secure collaborative few-shot learning
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“Another deep learning-based approach was proposed for cell classification and was compared against a bag of features and canonical representations methods and achieved better performance [8].”
BackgroundMethod
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“Recently, deep networks were successfully employed in a variety of medical image analysis applications, including nuclei detection in histopathology images of skin, breast and colon cancer [20, 22, 23], chest pathology detection in X-ray images and melanoma diagnosis [24–27].”
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“[15] used a deep learning architecture to discriminate between BCC and normal tissue models in 1417 images from 308 regions of interest (ROIs) of skin histopathology images.”
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“Patients usually receive chemotherapy before reaching the point where a stem-cell transplant is considered 18 .”
Classification of Histopathological Images Through Bag-of-Visual-Words and Gravitational Search Algorithm
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“Some works using CNNs show remarkably amazing results for cancer detection [14] and brain tumor segmentation [15].”
BackgroundMethod
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Moradi, P., & Jamzad, M. (2019) · doi:10.1109/pria.2019.8785971 · Cited by 36
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“Finally, although PDGNet obtain high performance on predicting disease genes, there is a poor interpretability [71] with PDGNet that most of current neural networks have.”
Method
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Background
Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning
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“[4] proposed a deep learning architecture through which the technique exploited the features in the intermediate layers as digital staining and used it on on basal-cell carcinoma detection whereas Wieslander et al.”
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“The researchers of [83] deployed SSAE with CNN to achieve highlevel functionality in a similar manner.”
Method
Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification
AlZubaidi, A. K., Sideseq, F. B., Hussein, A. F., & Basil, M. (2017) · Annual Conference on New Trends in Information & Communications Technology Applications (NTICT) · doi:10.1109/ntict.2017.7976109 · Cited by 33
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Deep Learning Diagnosis of Pigmented Skin Lesions
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“[9] built a model to detect basal-cell carcinoma.”
Background
Deep Domain Adaptation With Differential Privacy
Wang, Q., Li, Z., Zou, Q., Zhao, L., & Wang, S. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2983254 · Cited by 33
“It is worth noting that in some scenarios, the training data used for deep learning may be sensitive, e.g., the biomedical data about diseases [33], genetics [34] and biometrics [35], etc.”
Background
Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2
Leo, P., Chandramouli, S., Farré, X., Elliott, R. M., Janowczyk, A. R., Bera, K., Fu, P., Janaki, N., El‐Fahmawi, A., Shahait, M., et al. (2021) · European Urology Focus · doi:10.1016/j.euf.2021.04.016 · Cited by 32
Self-citation
Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images
Lu, C. Y., Xu, H., Xu, J., Gilmore, H. L., Mandal, M. K., & Madabhushi, A. (2016) · Scientific Reports · doi:10.1038/srep33985 · Cited by 31
“Recently a number of DL based approaches have been proposed for image analysis and classification applications in digital pathology 13,36 .”
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Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions11This work is an extension of the paper published by the same authors at the Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015) [1].
Carneiro, G., Nascimento, J. C., & Bradley, A. P. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00019-5 · Cited by 31
“However, MIA problems that have limited training sets have been generally tackled with the help of regularisation methods, such as unsupervised training [38, 39, 40, 41].”
Background
Deep Learning for Cardiovascular Risk Stratification
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Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study
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“Pre-operative PSA, ng/mL (median [Q1, Q3]) 8 (5, 11) 6 (5, 8) 6 (5, 9) 6 (5, 9) 6 (4, 8) 8 (6, 10) 8 (5, 13)”
Self-citation
Nucleus classification in histology images using message passing network
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Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images
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“These include detection of mitosis cell [12, 13], detection of carcinoma cancer [14, 15], segmentation of neuronal membranes [16], classification of skin lesion [17], detection of immune cell [18], segmentation of mass in mammograms [19, 20], tissue classification [21, 22] and so on.”
Method
Towards Explainable Graph Representations in Digital PathologyPreprint
Jaume, G., Pati, P., Foncubierta–Rodríguez, A., Feroce, F., Scognamiglio, G., Anniciello, A. M., Thiran, J., Göksel, O., & Gabrani, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00311 · Cited by 29
“Patch-level explainable methods (Graziani et al., 2018; H¨agele et al., 2019; Bruno et al., 2017; Mobadersany et al., 2017; Cruz-Roa et al., 2013; Xu et al., 2017) build patch-level heatmaps , where an importance score is computed per pixel to identify the regions of importance.”
Method
Mitochondrial Organelle Movement Classification (Fission and Fusion) via Convolutional Neural Network Approach
Iqbal, M. S., Luo, B., Mehmood, R., Alrige, M., & Alharbey, R. (2019) · IEEE Access · doi:10.1109/access.2019.2925041 · Cited by 28
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A nuclear circularity-based classifier for diagnostic distinction of desmoplastic from spindle cell melanoma in digitized histological images
Schöchlin, M., Weissinger, S. E., Brandes, A. R., Herrmann, M. D., Möller, P., & Lennerz, J. K. (2014) · Journal of Pathology Informatics · doi:10.4103/2153-3539.143335 · Cited by 28
Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation
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“Deep learning-based machine vision techniques are used in many disciplines, from speech, video, and audio processing [27], through to HAR [21] and cancer research [28].”
Background
Using deep learning to enhance head and neck cancer diagnosis and classification
Gupta, P., & Malhi, A. K. (2018) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan.2018.8541142 · Cited by 27
“[12] for automated basal cell carcinoma cancer detection.”
Method
Machine Learning Applications for Computer-Aided Medical Diagnostics
Oza, P., Sharma, P., & Patel, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0733-2_26 · Cited by 28
Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks
Tsuda, M., Asano, S., Kato, Y. P., Murai, K., & Miyazaki, M. (2019) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2019.04.014 · Cited by 25
Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives
Doeleman, T., Hondelink, L. M., Vermeer, M. H., van Dijk, M. R., & Schrader, A. M. R. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.004 · Cited by 26
Introduction to Deep Learning
Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2019) · Studies in big data · doi:10.1007/978-981-13-6794-6_1 · Cited by 28
DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting
Tyagi, A. K., Mohapatra, C., Das, P., Makharia, G. K., Mehra, L., Prathosh, A. P., & Mausam, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02290 · Cited by 26
“In the spirit of saving annotation effort, our work focuses on cell detection using annotated bounding boxes [5, 52].”
Method
On improving recurrent neural network for image classification
Chandra, B., & Sharma, R. K. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966083 · Cited by 26
“Deep learning has shown significant advancement in several fields such as Computer Vision [1, 2], Speech Recognition [3, 4], Natural Language Processing [5, 6] and Biomedical Applications [7, 8].”
Background
Deep Networks with Shape Priors for Nucleus Detection
Tofighi, M., Guo, T., Vanamala, J. K. P., & Monga, V. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451797 · Cited by 25
Double-Bit Quantization and Index Hashing for Nearest Neighbor Search
Xie, H., Mao, Z., Zhang, Y., Deng, H., Yan, C. C., & Chen, Z. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2872898 · Cited by 25
Artificial intelligence in dermatopathology: Updates, strengths, and challenges
Cazzato, G., & Rongioletti, F. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.010 · Cited by 24
Draw with me
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“…variants of DNN-based algorithms have been studied for image inpainting, e.g. Deep Convolution Generative Adversarial Networks (DCGANs) [19] and Deep Convolution Au-toencoder (DCA) [5], and achieve the state of the art performance on benchmarks for content-aware image inpainting tasks [14, 30].”
Background
AM-UNet: automated mini 3D end-to-end U-net based network for brain claustrum segmentation
Albishri, A., Shah, S. J. H., Kang, S. S., & Lee, Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11568-7 · Cited by 24
“…with image segmentation technologies, have been applied to medical imaging and automated segmentation [31] for a broad range of studies, from cancer detection [13] to brain tumor segmentation [23] and treatment and prevention of brain aging-related conditions, Alzheimer’s disease (AD) [51].”
Method
Lung cancer medical image recognition using Deep Neural Networks
Jakimovski, G., & Davcev, D. P. (2018) · International Conference on Digital Information Management · doi:10.1109/icdim.2018.8847136 · Cited by 24
“Image recognition in Deep Neural Networks is based on image classification, where the Neural Network is trained to classify an image into a list of predetermined piles or types, [4].”
BackgroundMethodInfluential
Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence
Arshad, M., Khan, M. A., Almujally, N. A., Alasiry, A., Marzougui, M., & Nam, Y. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03051-2 · Cited by 23
“Several kinds of skin cancer, such as Squamous cell carcinoma [12], basal cell carcinoma [13], and Merkel cell carcinoma [14], are distinct from melanoma.”
Application research of digital media image processing technology based on wavelet transform
Zhang, L., Zhang, L., & Zhang, L. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0383-6 · Cited by 23
“However, this kind of results mainly focus on methods, such as deep learning [20, 21], genetic algorithm [22, 23], fuzzy theory, etc.”
Background
V‐Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models
Caballero, H. S. G., Westenberg, M. A., Gebre, B. G., & van Wijk, J. J. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13667 · Cited by 24
Large scale digital prostate pathology image analysis combining feature extraction and deep neural networkPreprint
Zhou, N., Fedorov, A., Fennessy, F. M., Kikinis, R., & Gao, Y. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02678 · Cited by 23
“CNNs have also been applied to medical image analysis and have achieved much success[13,17,18,27].”
Method
Deep learning for tibial plateau fracture detection and classification
van der Gaast, N., Bagave, P., Assink, N., Broos, S., Jaarsma, R. L., Edwards, M. J., Hermans, E., IJpma, F. F. A., Ding, A. Y., Doornberg, J. N., et al. (2025) · The Knee · doi:10.1016/j.knee.2025.02.001 · Cited by 22
Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening
Das, D. K., Koley, S., Bose, S., Maiti, A. K., Mitra, B., Mukherjee, G., & Dutta, P. K. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105642 · Cited by 25
Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images
Javed, S., Mahmood, A., Dias, J. M. M., Werghi, N., & Rajpoot, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102104 · Cited by 23
Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma
Bungărdean, R. M., Şerbănescu, M.-S., Streba, C. T., & Crişan, M. (2022) · Romanian Journal of Morphology and Embryology · doi:10.47162/rjme.62.4.14 · Cited by 22
“such as epidermis or various glands [12].”
BackgroundInfluential
A hierarchal BoW for image retrieval by enhancing feature salience
Jiang, F., Hu, H., Zheng, J. Y., & Li, B. H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.044 · Cited by 22
Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization
Agarwal, R., Shekhawat, N. S., Kumar, S., Nayyar, A., & Qureshi, B. (2021) · IEEE Access · doi:10.1109/access.2021.3135536 · Cited by 21
“Some popular machine learning approaches in this category are convolutional neural network (CNN) [14], restricted Boltzmann machines [15], and auto-encoders [16].”
Background
[Retracted] Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task
Chopra, P., Junath, N., Singh, S. K., Khan, S., Sugumar, R., & Bhowmick, M. (2022) · BioMed Research International · doi:10.1155/2022/6336700 · Cited by 21
“Literature [10] used a single-task CNN model to train two CNN (convolutional neural network).”
Method
Retracted
Autofluorescence Image Reconstruction and Virtual Staining for In-Vivo Optical Biopsying
Picón, A., Medela, A., Sánchez‐Peralta, L. F., Cicchi, R., Bilbao, R., Alfieri, D., Elola, A., Glover, B. M., & Saratxaga, C. L. (2021) · IEEE Access · doi:10.1109/access.2021.3060926 · Cited by 23
Attention-guided deep convolutional neural networks for skin cancer classification
Aggarwal, A., Das, N., & Indu, S. (2019) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2019.8936100 · Cited by 20
E-Tanh: a novel activation function for image processing neural network models
Kalaiselvi, T., Padmapriya, S., Somasundaram, K., & Praveenkumar, S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07245-x · Cited by 20
“…and classiﬁcation [19], speech recognition [20, 21], segmentation [22, 23], understanding and describing a scenario [24, 25], machine translation [26, 27], speech systems [28, 29], cancer detection [30–32] biometrics [33, 34], forecasting weather [35, 36], automatic self-driving cars [37, 38].”
Background
Three class emotions recognition based on deep learning using staked autoencoder
Yang, B., Han, X., & Tang, J. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8302098 · Cited by 20
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Background
Deep Learning in BioinformaticsPreprint
Min, S., Lee, B., & Yoon, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06430 · Cited by 20
Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4Preprint
Zhou, J., He, X., Sun, L., Xu, J., Chen, X., Chu, Y., Zhou, L., Liao, X., Zhang, B., & Gao, X. (2023) · medRxiv · doi:10.1101/2023.06.10.23291127 · Cited by 20
Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions
Eldrandaly, K. A., Abdel‐Basset, M., Ibrahim, M., & AbdelAziz, N. M. (2022) · Enterprise Information Systems · doi:10.1080/17517575.2022.2098537 · Cited by 19
“(Cruz-Roa et al. 2013) created a unified technique for learning picture representations that extends DNN architecture to contain an interpretability layer to emphasise visual patterns where benefit the differentiation of cancer and normal tissues Their method is comparable to digital staining in…”
Method
Early detection of skin cancer using AI: Deciphering dermatology images for melanoma detection
Deepa, R., AlMahadin, G., C, P. G., & Sivasamy, A. (2024) · AIP Advances · doi:10.1063/5.0188187 · Cited by 20
Privacy preservation in Distributed Deep Learning: A survey on Distributed Deep Learning, privacy preservation techniques used and interesting research directions
Antwi-Boasiako, E., Zhou, S., Liao, Y., Liu, Q., Wang, Y., & Owusu-Agyemang, K. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102949 · Cited by 31
Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
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“Reference [61] proposed a multimodal medical imaging diagnostic model, which unifed the imaging model and language model in the deep learning framework and established a mapping relationship between the two modalities of medical imaging and diagnostic report.”
Method
Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images
Liu, Y., Feng, Y., Qian, L., Wang, Z., & Hu, X. (2023) · Experimental Biology and Medicine · doi:10.1177/15353702231220664 · Cited by 18
“12,14 However, a challenge with the widespread adoption of AI in healthcare has been its “black box” nature.”
Background
A dense multi-path decoder for tissue segmentation in histopathology images
Vu, Q. D., & Kwak, J. T. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.03.007 · Cited by 18
A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation
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“tumor and mitosis detection) and relatively few approaches are geared towards unsupervised learning.(7,8)”
BackgroundSelf-citation
Enhancing Histopathological Colorectal Cancer Image Classification by using Convolutional Neural NetworkPreprint
Al.Shawesh, R., & Chen, Y. X. (2021) · medRxiv · doi:10.1101/2021.03.17.21253390 · Cited by 17
Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2019) · PubMed Central · doi:10.48550/arxiv.1911.11872 · Cited by 16
“ore. They introduced a deep segmentation network for in-depth analysis of the hard cases to further improve the performance with 0.987 AUC, 0.97 sensitivity, 0.94 specicity score. 3. Cruz-Roa et al. [45] used a deep learning architecture to discriminate between BCC and normal tissue patterns on 1,417 images from 308 R…”
BackgroundInfluential

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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