Mr. Angel A. Cruz Roa Profile

Angel A. Cruz Roa

at Univ de los Llanos

SPIE Involvement:

Author

Area of Expertise:

Medical imaging , Image processing , Digital pathology , Machine learning , Image understanding

Websites:

Personal Website | Company Website

Angel Cruz-Roa received his B.S. degree in systems engineering from University of the Llanos, Villavicencio (Colombia), in 2005, the M.S. degree in biomedical engineering from National University of Colombia, Bogota (Colombia), in 2011.
He is currently a Ph.D Candidate in the Doctorate in Systems and Computing Engineering in MindLab Research Group, National University of Colombia, Bogota (Colombia). He have been visitor researcher in The Center of Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, Cleveland, OH (E.E.U.U.) and CETA-Ciemat at Trujillo (Spain).
His research topics of interest are image analysis and understanding, data mining and analytics, and machine learning applications in biomedical imaging and digital pathology. Currently his Ph.D research project addresses to develop computerized methods to provide visual interpretation and knowledge discovery as support to clinical decisions and research in cancer from (large) biomedical image databases.

Proceedings Article | 4 April 2022 Presentation + Paper

Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach

Jennifer Salguero L., Prateek Prasanna, Germán Corredor, Angel Cruz-Roa, David Becerra, Eduardo Romero

Proceedings Volume 12039, 1203906 (2022) https://doi.org/10.1117/12.2612984

KEYWORDS: Ovarian cancer, Cancer, Statistical modeling, Pathology, Feature extraction, Diagnostics, Machine learning, Statistical analysis

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Proceedings Article | 10 December 2021 Paper

A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder

Fabian Cano, Charlens Alvarez-Jimenez, David Becerra, Andres Siabatto, Angel Cruz-Roa, Eduardo Romero

Proceedings Volume 12088, 120880Y (2021) https://doi.org/10.1117/12.2606255

KEYWORDS: Tissues, Lung cancer, Cancer, Data modeling, Statistical analysis, Tumors, Computer programming, Tumor growth modeling, Visualization, Statistical modeling

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Proceedings Article | 3 January 2020 Paper

An exploratory study of one-shot learning using Siamese convolutional neural network for histopathology image classification in breast cancer from few data examples

Fabian Cano, Angel Cruz-Roa

Proceedings Volume 11330, 113300A (2020) https://doi.org/10.1117/12.2546488

KEYWORDS: Breast cancer, Image classification, Convolutional neural networks, Pathology, Digital imaging

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Proceedings Article | 21 December 2018 Paper

A comparative analysis of sensitivity of convolutional neural networks for histopathology image classification in breast cancer

Fabian Cano, Anant Madabhushi, Angel Cruz-Roa

Proceedings Volume 10975, 109750W (2018) https://doi.org/10.1117/12.2511647

KEYWORDS: Breast cancer, Neurons, Data modeling, Tumor growth modeling, Pathology, Cancer, Digital imaging, Convolutional neural networks, Visual process modeling, Image classification

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Proceedings Article | 22 December 2015 Paper

A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

Angel Cruz-Roa, John Arévalo, Alexander Judkins, Anant Madabhushi, Fabio González

Proceedings Volume 9681, 968103 (2015) https://doi.org/10.1117/12.2208825

KEYWORDS: Convolutional neural networks, Visualization, Tumors, Visual process modeling, Data modeling, Feature extraction, RGB color model, Tissues, Tumor growth modeling, Machine vision

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Proceedings Article | 28 January 2015 Paper

A note on the stability and discriminability of graph-based features for classification problems in digital pathology

Angel Cruz-Roa, Jun Xu, Anant Madabhushi

Proceedings Volume 9287, 928703 (2015) https://doi.org/10.1117/12.2085141

KEYWORDS: Image segmentation, Cancer, Pathology, Tumors, Tissues, Feature extraction, Breast cancer, Image processing algorithms and systems, Detection and tracking algorithms, Prostate

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Nuclear architecture or the spatial arrangement of individual cancer nuclei on histopathology images has been shown to be associated with different grades and differential risk for a number of solid tumors such as breast, prostate, and oropharyngeal. Graph-based representations of individual nuclei (nuclei representing the graph nodes) allows for mining of quantitative metrics to describe tumor morphology. These graph features can be broadly categorized into global and local depending on the type of graph construction method. While a number of local graph (e.g. Cell Cluster Graphs) and global graph (e.g. Voronoi, Delaunay Triangulation, Minimum Spanning Tree) features have been shown to associated with cancer grade, risk, and outcome for different cancer types, the sensitivity of the preceding segmentation algorithms in identifying individual nuclei can have a significant bearing on the discriminability of the resultant features. This therefore begs the question as to which features while being discriminative of cancer grade and aggressiveness are also the most resilient to the segmentation errors. These properties are particularly desirable in the context of digital pathology images, where the method of slide preparation, staining, and type of nuclear segmentation algorithm employed can all dramatically affect the quality of the nuclear graphs and corresponding features. In this paper we evaluated the trade off between discriminability and stability of both global and local graph-based features in conjunction with a few different segmentation algorithms and in the context of two different histopathology image datasets of breast cancer from whole-slide images (WSI) and tissue microarrays (TMA). Specifically in this paper we investigate a few different performance measures including stability, discriminability and stability vs discriminability trade off, all of which are based on p-values from the Kruskal-Wallis one-way analysis of variance for local and global graph features. Apart from identifying the set of local and global features that satisfied the trade off between stability and discriminability, our most interesting finding was that a simple segmentation method was sufficient to identify the most discriminant features for invasive tumour detection in TMAs, whereas for tumour grading in WSI, the graph based features were more sensitive to the accuracy of the segmentation algorithm employed.

Proceedings Article | 28 January 2015 Paper

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

Angel Cruz-Roa, John Arevalo, Ajay Basavanhally, Anant Madabhushi, Fabio González

Proceedings Volume 9287, 92870G (2015) https://doi.org/10.1117/12.2073849

KEYWORDS: Tumors, Machine learning, Neurons, Tumor growth modeling, Independent component analysis, Pathology, Sensors, Pattern recognition, Feature extraction, Convolutional neural networks

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Proceedings Article | 28 January 2015 Paper

Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

Germán Sosa, Angel Cruz-Roa, Fabio González

Proceedings Volume 9287, 928709 (2015) https://doi.org/10.1117/12.2073614

KEYWORDS: Lung, Feature extraction, Fourier transforms, Wavelets, Acoustics, Data modeling, Electronic filtering, Machine learning, Chronic obstructive pulmonary disease, Speech recognition

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SPIE Journal Paper | 10 October 2014

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

Haibo Wang, Angel Cruz Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio Gonzalez, Anant Madabhushi

JMI, Vol. 1, Issue 03, 034003, (October 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.3.034003

KEYWORDS: Breast cancer, Pathology, Feature extraction, Image segmentation, Computing systems, Neurons, RGB color model, Scanners, Convolutional neural networks, Microscopes

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Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 HPFs (400× magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Our approach is accurate, fast, and requires fewer computing resources compared to existent methods, making this feasible for clinical use.

Proceedings Article | 20 March 2014 Paper

Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

Haibo Wang, Angel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio Gonzalez, Anant Madabhushi

Proceedings Volume 9041, 90410B (2014) https://doi.org/10.1117/12.2043902

KEYWORDS: Feature extraction, RGB color model, Computing systems, Scanners, Breast cancer, Data modeling, Microscopes, Image segmentation, Convolutional neural networks, Glasses

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Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 High Power Fields (HPF, x400 magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Apart from this being the second best performance ever recorded for this MITOS dataset, our approach is faster and requires fewer computing resources compared to extant methods, making this feasible for clinical use.

Proceedings Article | 20 March 2014 Paper

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

Angel Cruz-Roa, Ajay Basavanhally, Fabio González, Hannah Gilmore, Michael Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi

Proceedings Volume 9041, 904103 (2014) https://doi.org/10.1117/12.2043872

KEYWORDS: Tissues, Visualization, Tumors, Image segmentation, Convolution, Breast cancer, RGB color model, Pathology, Convolutional neural networks, Tumor growth modeling

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This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.

Proceedings Article | 19 November 2013 Paper

Hybrid image representation learning model with invariant features for basal cell carcinoma detection

John Arevalo, Angel Cruz-Roa, Fabio González

Proceedings Volume 8922, 89220M (2013) https://doi.org/10.1117/12.2035530

KEYWORDS: Visualization, Tumor growth modeling, Visual process modeling, Associative arrays, Independent component analysis, Tissues, Machine vision, Neodymium, Data modeling, Computer vision technology

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Proceedings Article | 19 November 2013 Paper

Bag-of-visual-ngrams for histopathology image classification

A. Pastor López-Monroy, Manuel Montes-y-Gómez, Hugo Jair Escalante, Angel Cruz-Roa, Fabio González

Proceedings Volume 8922, 89220P (2013) https://doi.org/10.1117/12.2034113

KEYWORDS: Visualization, Image classification, Machine vision, Computer vision technology, Visual process modeling, Information visualization, Visual analytics, Binary data, Image processing, Image retrieval

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Proceedings Article | 29 March 2013 Paper

An adaptive image representation learned from data for cervix cancer tumor detection

Angel Cruz Roa, Eduardo Romero, Fabio González

Proceedings Volume 8676, 86760Q (2013) https://doi.org/10.1117/12.2007081

KEYWORDS: Associative arrays, Tumors, Visualization, Cancer, Cervix, Pathology, Cervical cancer, Tissues, Image analysis, Feature extraction

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