Deep learning algorithm for cervical cancer detection based on images of optomagnetic spectra

In order to further investigate performance of Optomagnetic Imaging Spectroscopy in cervical cancer detection, deep learning algorithm has been used for classification of optomagnetic spectra of the samples. Optomagnetic spectra reflect cell properties and based on those properties it is possible to differ-entiate normal cells from cells showing different levels of dysplasia and cancer cells. In one of the previous research, Optomagnetic imaging spectroscopy has demonstrated high percentages of accuracy, sensitivity and specificity in cervical cancer detection, particularly in the case of binary classification. Somewhat lower accuracy percentages were obtained in the case of four class classification. Compared to the results obtained by conventional machine learning classification algorithms, proposed deep learning algorithm achieves similar accuracy results (80%), greater sensitivity (83.3%), and comparable specificity percentages (78%)

Classification of squamous cell cervical cytology

Cervical cancer occurs significantly in women in developing countries every day and produces a high number of casualties, with a large economic and social cost. The World Health Organization, in the right against cervical cancer, promotes early detection screening programs by difeerent detection techniques such as conventional cytology (Pap), cytology liquid medium (CML), DNA test Human Papillomavirus (HPV), staining with dilute acetic acid and Lugol's iodine solution. Conventional cytology is the most used technique, being widely accepted, inexpensive, and with quality control mechanisms. The test has shown a sensitivity of 38% to 84% and a specificity of 90% in multiple studies and has been considered as the choice test for screening [14]. The cervical cancer is not a public health problems in developed countries since more than three decades, among others because of implementation of other tests such as the CML which has increased the sensitivity to a figures that vary between 76% and 99 %. This test in particular produces a thin monolayer of cells that are examined. In our countries this technique is really far from being applied because of its high cost. In consequence, the conventional cytology has remained in practice as the only possible examination of the cervix pathology. In this technique, a sample of cells from the transformation zone of the cervix is taken, using a brush or wooden spatula, spread onto a slide and fixed with a preservative solution. This sample is then sent to a laboratory for staining and microscopic examination to determine whether cells are normal or not. This task requires time and expertise for the diagnosis. Attempting to alleviate the work burden from the number of examinations in clinical routine scenario, some researchers have proposed the development of computational tools to detect and classify the cells of the transformation cervix zone. In the present work the transformation zone is firstly characterized using color and texture descriptors defined in the MPEG-7 standard, and the tissue descriptors are used as the input to a bank of binary classifiers, obtaining a precision of 90% and a sensitivity of 83 %. Unlike traditional approaches that extract cell features from previously segmented cells, the present strategy is completely independent of the particular shape. Yet most works in the domain report higher precision rates, the images used in these works for training and evaluation are really different from what is obtained in the cytology laboratories in Colombia. Overall, most of these methods are applied to monolayer techniques and therefore the recognition rates are better from what we found in the present investigation. However, the main aim of the present work was thus to develop a strategy applicable to our real conditions as a pre-screening method, case in which the method should be robust to many random factors that contaminate the image capture. A segmentation strategy is very easily misleaded by all these factor so that our method should use characteristics independently of the segmentation quality, while the reading time is minimized, as well as the intra-observer variability, facilitating thereby real application of such screening tools.Maestrí

Otimização de descritores usados nos estudos de cambios associadas à malignidade em imagens digitais de células cervicais

Orientadores: Marco Antonio Garcia de Carvalho, Guilherme Palermo CoelhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: O Câncer de Colo de Útero (CCU) é um problema de saúde coletiva em todo o mundo, nesse sentido foram feitos grandes avanços para sua detecção e prevenção. Apesar dos esforços feitos pelos países da América Latina para reduzir os indicadores de mortes por essa doença, eles ainda não são suficientes em comparação com o progresso de outros países europeus.Uma das razões, é que os sistemas de saúde pública em vários países da América têm limitações importantes em seus programas de acompanhamento e prevenção.O vírus do papanicolau está associado a 95 % dos cânceres cervicais, embora as instituições de saúde pública em todo o mundo invistam esforços técnicos, humanos e econômicos para reduzir o impacto da CCU em suas comunidades. Desde 1960, são realizadas pesquisas a respeito ao exame do Papanicolau, considerado este como um dos mecanismos mais utilizados pelo mundo para controlar e diagnosticar esta doença. Alterações Associadas à Malignidade (MAC), são pequenas alterações na morfologia e textura da cromatina, predizendo possíveis lesões malignas associadas ao CCU, tornando-se uma investigação interessante na aplicação do exame do panicolau. A identificação de MAC¿s em imagens de células cervicais é um problema accessível a possíveis investigações, devido às complexidades da identificação visual de estruturas nucleares. A partir das técnicas de Processamento Digital de Imagens (PDI), tem se conseguido grandes avanços, especialmente na obtenção de 400 descritores para o estudo de MAC's, no entanto a pequena quantidade de imagens focadas no estudo MAC, assim como a limitação técnica do equipamento e poucos profissionais que trabalham nesses estudos limitam o progresso nesta área. Esta tese tem como objetivo, otimizar descritores propostos na literatura para o estudo do MAC utilizando PDI. Para atingir este objetivo, foi criado em conjunto com a Fundação Universitária de Ciências para a Saúde da Colômbia (FUCS), um Data set de imagens de células cervicais que possibilitará o estudo de MAC's. Para adquirir imagens para o estudo, foram digitalizadas 6 folhas de pacientes com diferentes patologias que foram diagnosticadas e marcadas por uma cito-técnica especializada. As imagens foram pré-processadas empregando filtros espaciais e núcleos segmentados usando o algoritmo k-means e watershed. Os canais de cor foram separados pela sua contribuição de hematoxilina e corante Orange G6 dos núcleos segmentados; se extraíram 800 descritores morfológicos, de textura, densidade óptica e iluminação dos núcleos para sua posterior classificação. Contribuímos com a criação de um conjunto de dados para o estudo do MAC em imagens de CCU de exames de citologia convencional. Comparamos três classificadores supervisionados, treinados com 795 descritores, 412 descritores, 200 descritores e 962 instâncias. Calculamos e ordenamos os descritores extraídos pela informação obtida de cada um deles. Com um grupo de descritores, a precisão da classificação é 95,3 %. A segmentação dos núcleos mostrou uma precisão de 85,6 %. A otimização dos descritores foi de 4,3% melhor que a dos descritores propostos pela literatura, sendo composta por 30% de descritores de textura, 27% de descritores morfológicos, 11,5% de descritores de densidade óptica e 17% de descritores associados à concordância de níveis de cinzaAbstract: Cervical cancer (CCU) is a collective health problem worldwide, in that sense great advances have been made for its detection and prevention. Despite the efforts made by Latin American countries to reduce the indicators of deaths from this disease, they are still not sufficient compared to the progress of other European countries. One of the reasons is that the public health systems of several countries in the Americas present important limitations in their monitoring and prevention programs. The Human Papilloma Virus is associated with 95% of cervical cancers. Public health institutions around the world invest technical, human, and economic efforts to lessen the impact of the CCU on their communities. The mechanism most used by the world to control and diagnose this disease is the examination of the Human Papilloma. Research on this test has been conducted since 1960. The Malignancy Associated Changes MAC, are slight alterations in the morphology and texture of chromatin predicting possible malignant lesions associated to CCU, becoming one of the promising researches to be applied in the examination of the human papilloma. The identification of MAC's in cervical cell images is an open problem, due to the complexities of visual identification of nuclear structures. From Digital Image Processing (DIP) techniques great advances have been made especially in obtaining 400 descriptors for the study of MAC's, however the small amount of images focused on MAC's study, as well as the technical limitation of the equipment and few professionals who worked to these studies has limited progress in this area. The objective of this thesis is to optimize the descriptors proposed in the literature for the study of MAC using DIP. In order to achieve this objective, a set of cervical cell images was created for the study of MAC's, in conjunction with the Fundación Universitaria de Ciencias para la Salud-Colombia (FUCS). With the purpose of acquiring images for the study, 6 slides of patients with different pathologies were digitalized, which were diagnosed and labeled by a specialized cyto-technique. The images were pre-processed using spatial filters and segmented nuclei using the k-means and watershed algorithm. The color channels were separated by contribution of Hematoxylin and Orange G6 dye from the segmented nuclei; 800 morphological, texture, optical density and illumination descriptors were extracted from the nuclei for later classification. We contributed with the creation of a Data Set for the study of MAC in CCU images of conventional cytology examinations. We compared three supervised classifiers with 795 descriptors, 412 descriptors, 200 descriptors and 962 instances. We calculated and sorted the extracted descriptors by the information gain of each one of them. The optimization of the descriptors was 4.3% better than the descriptors proposed in the literature, consisting of 30% texture descriptors, 27% morphological descriptors, 11.5% optical density descriptors and 17% descriptors associated with the agreement of gray levelsDoutoradoSistemas de Informação e ComunicaçãoDoutor em TecnologiaCAPE

Clasificación automática de tumores de ovario y citologías cervicovaginales a partir de imágenes ecográficas y microscópicas mediante su análisis con técnicas de aprendizaje automático.

Aplicar técnicas de Aprendizaje Automático para la clasificación diagnóstica de imágenes ecográficas de tumores de ovario e imágenes microscópicas de citologías cervicovaginales teñidas con la técnica de Papanicolaou. Aplicar técnicas de Aprendizaje Automático para la clasificación de descriptores geométricos obtenidos de imágenes ecográficas de tumores ováricos mediante Fast Fourier Transform y comparar con los resultados obtenidos con otros trabajos que utilizan la misma base de datos. Crear una base de datos mediante fusión de imágenes para mezclar y superponer las células originales y aumentando el número de imágenes disponibles para clasificación. Aplicar Deep Learning para la clasificación de las imágenes microscópicas de la base de datos de nueva creación y comparar los resultados con los obtenidos en la clasificación de la base de datos original. METODO Se realizará el análisis de resultados de la aplicación de clasificadores basados en Aprendizaje Automático clásicos sobre una base de datos de descriptores extraídos mediante Transformada de Fourier a partir de una colección de 187 imágenes ecográficas de tumores de ovario, 112 benignos y 75 malignos, cedidos por la Universidad de Buckingham, que a su vez obtuvo las imágenes originales de la Universidad Católica de Leuven. Las características que se clasifican son Histogramas de Intensidad y Descriptores de Patrón Binario Local. También se realizará la clasificación de imágenes microscópicas de células escamosas cervicovaginales teñidas mediante la técnica de Papanicolaou aplicando una red neuronal convolucional. La muestra es creada a partir de 10 citologías cervicovaginales procedentes del Servicio de Anatomía Patológica del Complejo Hospitalario Universitario Santa Lucía-Santa María del Rosell (Cartagena, Murcia), de donde se extrajeron 1405 células. 450 normales, 323 ASC-US, 213 L-SIL, 419 H-SIL. Además, a partir de estas células se desarrolla un sistema de fusión de imágenes para aumentar el nú mero de imágenes de la muestra, obteniendo 20.000 imágenes por cada categoría, 80.000 en total. RESULTADOS La clasificación de características descriptivas de imágenes ecográficas de tumores de ovario ofrece resultados similares a los obtenidos por un observador experimentado cuando se aplica los métodos Linear Discriminant, Support Vector Machine y Extreme Learning Machine. La clasificación de células escamosas cervicales mediante Deep Learning ofrece resultados interesantes, que mejoran al aumentar el tamaño muestral de entrenamiento aunque estas imágenes sean más complejas por la fusión, obteniendo resultados comparables a los obtenidos por patólogos expertos. CONCLUSIONES Los métodos basados en Inteligencia Artificial pueden tener utilidad para el disenño de sistemas de ayuda al diagnóstico médico asistidos por ordenador, para la clasificación de imágenes ecográficas de tumores de ovario, así como para la detección de células escamosas cervicales atípicas procedentes de citologías cervicovaginales teñidas mediante la técnica de Papanicolaou. Los descriptores Geométricos obtenidos mediante Fast Fourier Transform aportan información útil y relevante para la clasificación de ecografías de tumores de ovario, mejorando los resultados obtenidos en nuestro estudio en comparación con otros estudios anteriores realizados sobre la misma base de datos. Se ha generado una base de datos mediante fusión de imágenes con transparencia a partir de la base de datos original de células escamosas cervicovaginales, obteniendo una colección de 80.000 imágenes de nueva creación, con mayor complejidad. Al aplicar las técnicas de Deep Learning sobre la base de datos de nueva creación, se comprueba que aumentando el número de imágenes de muestra, independientemente de su complejidad, se mejora los resultados y la estabilidad de la clasificación.Medicin

The Potential of Raman Spectroscopy for Cytological Diagnosis of ThinPrep Samples From a Cervical Cancer Screening Population

Cervical cancer is the second most common cancer in women worldwide. For decades, most developed countries have applied organized cytology screening programs using the Papanicolau (PAP) test to identify abnormal cases. Despite a high specificity of 95-98%, Pap test sensitivity is reported to vary greatly from 74 to 96% with constant testing needed to achieve the highest values. Semi-automated cytology screening platforms, immunocytochemistry panels and other methodologies such as human papilloma virus (HPV) testing has been developed to help reduce false negative rates. More recently, HPV testing, thus far used for triage of abnormal cytology cases and test of cure, have been recommended for primary screening. However HPV testing only informs on the presence of the virus not adding on the abnormal transformation of the cells. Its suitability for screening of younger women (years) whom present the highest rates of transient viral infection is also questionable. The potential of Raman spectroscopy has also been acknowledged with its ability of detecting spectral changes in malignant and pre-malignant cells extensively reported. In this project the potential of Raman spectroscopy for cytological diagnosis of samples from a cervical cancer screening population was assessed. Routinely used ThinPrep® glass slides were used as spectroscopy substrates in order to minimize costs and simplify sample processing. Raman spectra were recorded from single cell nuclei and subjected to multivariate statistical analysis. Different approaches were tested to minimize the glass contribution to the sample spectra and a non-negative least squares method was found to provide the best results. Normal and abnormal ThinPrep® samples were discriminated based on their biochemical fingerprint using Principal Component Analysis (PCA). Principal Component Analysis – Linear Discriminant Analysis (PCA-LDA) was further employed to build classification models using both Cervical Intraepithelial Neoplasia (CIN) and Squamous Intraepithelial Lesion (SIL) terminology. Results showed that Raman spectroscopy can be successfully applied to the study of routine cervical cytology samples from a cervical screening programme and normal and abnormal samples could be discriminated with high sensitivity and specificity rates (\u3e95%) when tested with leave one patient out cross validation. In addition, the results suggested that HPV infection and previous disease history might be inferred from negative samples and might influence the performance of classifiers. In summary this study has shown Raman spectroscopy has potential as a screening tool for Thinprep® cervical cytology samples

HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC

This paper presents HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles. HALO implements a novel compute-centric message passing interface (C^2MPI) specification for enabling the performance-portable execution of a hardware-agnostic host application across heterogeneous accelerators. The experiment results of evaluating eight widely used HPC subroutines based on Intel Xeon E5-2620 CPUs, Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for host programs to run across all the computing devices with a consistently top performance portability score, which is up to five orders of magnitude higher than the OpenCL-based solution.Comment: 21 page

Representation learning for histopathology image analysis

Abstract. Nowadays, automatic methods for image representation and analysis have been successfully applied in several medical imaging problems leading to the emergence of novel research areas like digital pathology and bioimage informatics. The main challenge of these methods is to deal with the high visual variability of biological structures present in the images, which increases the semantic gap between their visual appearance and their high level meaning. Particularly, the visual variability in histopathology images is also related to the noise added by acquisition stages such as magnification, sectioning and staining, among others. Many efforts have focused on the careful selection of the image representations to capture such variability. This approach requires expert knowledge as well as hand-engineered design to build good feature detectors that represent the relevant visual information. Current approaches in classical computer vision tasks have replaced such design by the inclusion of the image representation as a new learning stage called representation learning. This paradigm has outperformed the state-of-the-art results in many pattern recognition tasks like speech recognition, object detection, and image scene classification. The aim of this research was to explore and define a learning-based histopathology image representation strategy with interpretative capabilities. The main contribution was a novel approach to learn the image representation for cancer detection. The proposed approach learns the representation directly from a Basal-cell carcinoma image collection in an unsupervised way and was extended to extract more complex features from low-level representations. Additionally, this research proposed the digital staining module, a complementary interpretability stage to support diagnosis through a visual identification of discriminant and semantic features. Experimental results showed a performance of 92% in F-Score, improving the state-of-the-art representation by 7%. This research concluded that representation learning improves the feature detectors generalization as well as the performance for the basal cell carcinoma detection task. As additional contributions, a bag of features image representation was extended and evaluated for Alzheimer detection, obtaining 95% in terms of equal error classification rate. Also, a novel perspective to learn morphometric measures in cervical cells based on bag of features was presented and evaluated obtaining promising results to predict nuclei and cytoplasm areas.Los métodos automáticos para la representación y análisis de imágenes se han aplicado con éxito en varios problemas de imagen médica que conducen a la aparición de nuevas áreas de investigación como la patología digital. El principal desafío de estos métodos es hacer frente a la alta variabilidad visual de las estructuras biológicas presentes en las imágenes, lo que aumenta el vacío semántico entre su apariencia visual y su significado de alto nivel. Particularmente, la variabilidad visual en imágenes de histopatología también está relacionada con el ruido añadido por etapas de adquisición tales como magnificación, corte y tinción entre otros. Muchos esfuerzos se han centrado en la selección de la representacion de las imágenes para capturar dicha variabilidad. Este enfoque requiere el conocimiento de expertos y el diseño de ingeniería para construir buenos detectores de características que representen la información visual relevante. Los enfoques actuales en tareas de visión por computador han reemplazado ese diseño por la inclusión de la representación en la etapa de aprendizaje. Este paradigma ha superado los resultados del estado del arte en muchas de las tareas de reconocimiento de patrones tales como el reconocimiento de voz, la detección de objetos y la clasificación de imágenes. El objetivo de esta investigación es explorar y definir una estrategia basada en el aprendizaje de la representación para imágenes histopatológicas con capacidades interpretativas. La contribución principal de este trabajo es un enfoque novedoso para aprender la representación de la imagen para la detección de cáncer. El enfoque propuesto aprende la representación directamente de una colección de imágenes de carcinoma basocelular en forma no supervisada que permite extraer características más complejas a partir de las representaciones de bajo nivel. También se propone el módulo de tinción digital, una nueva etapa de interpretabilidad para apoyar el diagnóstico a través de una identificación visual de las funciones discriminantes y semánticas. Los resultados experimentales mostraron un rendimiento del 92% en términos de F-Score, mejorando la representación del estado del arte en un 7%. Esta investigación concluye que el aprendizaje de la representación mejora la generalización de los detectores de características así como el desempeño en la detección de carcinoma basocelular. Como contribuciones adicionales, una representación de bolsa de caracteristicas (BdC) fue ampliado y evaluado para la detección de la enfermedad de Alzheimer, obteniendo un 95% en términos de EER. Además, una nueva perspectiva para aprender medidas morfométricas en las células del cuello uterino basado en BdC fue presentada y evaluada obteniendo resultados prometedores para predecir las areás del nucleo y el citoplasma.Maestrí