Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images

Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as the breast cancer pathology helps in the diagnosis and monitoring of the disease. However identification of malignant cells can be challenging given the high heterogeneity in tissue absorbotion from staining agents. In this work, we present a novel approach for Invasive Ductal Carcinoma (IDC) cells discrimination in histopathology slides. We propose a model derived from the Inception architecture, proposing a multi-level batch normalization module between each convolutional steps. This module was used as a base block for the feature extraction in a CNN architecture. We used the open IDC dataset in which we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus surpassing recent state of the art classification algorithms tested on this public dataset.Comment: 4 pages, 5 figure

Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis

Deep Learning for Classification of Brain Tumor Histopathological Images

Histopathological image classification has been at the forefront of medical research. We evaluated several deep and non-deep learning models for brain tumor histopathological image classification. The challenges were characterized by an insufficient amount of training data and identical glioma features. We employed transfer learning to tackle these challenges. We also employed some state-of-the-art non-deep learning classifiers on histogram of gradient features extracted from our images, as well as features extracted using CNN activations. Data augmentation was utilized in our study. We obtained an 82% accuracy with DenseNet-201 as our best for the deep learning models and an 83.8% accuracy with ANN for the non-deep learning classifiers. The average of the diagonals of the confusion matrices for each model was calculated as their accuracy. The performance metrics criteria in this study are our model’s precision in classifying each class and their average classification accuracy. Our result emphasizes the significance of deep learning as an invaluable tool for histopathological image studies

Data-driven Representation Learning from Histopathology Image Databases to Support Digital Pathology Analysis

Cancer research is a major public health priority in the world due to its high incidence, diversity and mortality. Despite great advances in this area during recent decades, the high incidence and lack of specialists have proven that one of the major challenges is to achieve early diagnosis. Improved early diagnosis, especially in developing countries, plays a crucial role in timely treatment and patient survival. Recent advances in scanner technology for the digitization of pathology slides and the growth of global initiatives to build databases for cancer research have enabled the emergence of digital pathology as a new approach to support pathology workflows. This has led to the development of many computational methods for automatic histopathology image analysis, which in turn has raised new computational challenges due to the high visual variability of histopathology slides, the difficulty in assessing the effectiveness of methods (considering the lack of annotated data from different pathologists and institutions), and the need of interpretable, efficient and feasible methods for practical use. On the other hand, machine learning techniques have focused on exploiting large databases to automatically extract and induce information and knowledge, in the form of patterns and rules, that allow to connect low-level content with its high-level meaning. Several approaches have emerged as opposed to traditional schemes based on handcrafted features for data representation, which nowadays are known as representation learning. The objective of this thesis is the exploration, development and validation of precise, interpretable and efficient computational machine learning methods for automatic representation learning from histopathology image databases to support diagnosis tasks of different types of cancer. The validation of the proposed methods during the thesis development allowed to corroborate their capability in several histopathology image analysis tasks of different types of cancer. These methods achieve good results in terms of accuracy, robustness, reproducibility, interpretability and feasibility suggesting their potential practical application towards translational and personalized medicine.Resumen. La investigación en cáncer es una de las principales prioridades de salud pública en el mundo debido a su alta incidencia, diversidad y mortalidad. A pesar de los grandes avances en el área en las últimas décadas, la alta incidencia y la falta de especialistas ha llevado a que una de las principales problemáticas sea lograr su detección temprana, en especial en países en vías de desarrollo, como quiera a que de ello depende las posibilidades de un tratamiento oportuno y las oportunidades de supervivencia de los pacientes. Los recientes avances en tecnología de escáneres para digitalización de láminas de patología y el crecimiento de iniciativas mundiales para la construcción de bases de datos para la investigación en cáncer, han permitido el surgimiento de la patología digital como un nuevo enfoque para soportar los flujos de trabajo en patología. Esto ha llevado al desarrollo de una gran variedad de métodos computacionales para el análisis automático de imágenes de histopatología, lo cual ha planteado nuevos desafíos computacionales debido a la alta variabilidad visual de las láminas de histopatología; la dificultad para evaluar la efectividad de los métodos por la falta de datos de diferentes instituciones que cuenten con anotaciones por parte de los patólogos, y la necesidad de métodos interpretables, eficientes y factibles para su uso práctico. Por otro lado, el aprendizaje de máquina se ha enfocado en explotar las grandes bases de datos para extraer e inducir de manera automática información y conocimiento, en forma de patrones y reglas, que permita conectar el contenido de bajo nivel con su significado. Diferentes técnicas han surgido en contraposición a los esquemas tradicionales basados en diseño manual de la representación de los datos, en lo que se conoce como aprendizaje de la representación. El propósito de esta tesis fue la exploración, desarrollo y validación de métodos computacionales de aprendizaje de máquina precisos, interpretables y eficientes a partir de bases de datos de imágenes de histopatología para el aprendizaje automático de la representación en tareas de apoyo al diagnóstico de distintos tipos de cáncer. La validación de los distintos métodos propuestos durante el desarrollo de la tesis permitieron corroborar la capacidad de cada uno de ellos en distintivas tareas de análisis de imágenes de histopatología, en diferentes tipos de cáncer, con buenos resultados en términos de exactitud, robustez, reproducibilidad, interpretabilidad y factibilidad, lo cual sugiere su potencial aplicación práctica hacia la medicina traslacional y personalizada.Doctorad

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph

Machine Learning Approaches to Predict Recurrence of Aggressive Tumors

Cancer recurrence is the major cause of cancer mortality. Despite tremendous research efforts, there is a dearth of biomarkers that reliably predict risk of cancer recurrence. Currently available biomarkers and tools in the clinic have limited usefulness to accurately identify patients with a higher risk of recurrence. Consequently, cancer patients suffer either from under- or over- treatment. Recent advances in machine learning and image analysis have facilitated development of techniques that translate digital images of tumors into rich source of new data. Leveraging these computational advances, my work addresses the unmet need to find risk-predictive biomarkers for Triple Negative Breast Cancer (TNBC), Ductal Carcinoma in-situ (DCIS), and Pancreatic Neuroendocrine Tumors (PanNETs). I have developed unique, clinically facile, models that determine the risk of recurrence, either local, invasive, or metastatic in these tumors. All models employ hematoxylin and eosin (H&E) stained digitized images of patient tumor samples as the primary source of data. The TNBC (n=322) models identified unique signatures from a panel of 133 protein biomarkers, relevant to breast cancer, to predict site of metastasis (brain, lung, liver, or bone) for TNBC patients. Even our least significant model (bone metastasis) offered superior prognostic value than clinopathological variables (Hazard Ratio [HR] of 5.123 vs. 1.397 p\u3c0.05). A second model predicted 10-year recurrence risk, in women with DCIS treated with breast conserving surgery, by identifying prognostically relevant features of tumor architecture from digitized H&E slides (n=344), using a novel two-step classification approach. In the validation cohort, our DCIS model provided a significantly higher HR (6.39) versus any clinopathological marker (p\u3c0.05). The third model is a deep-learning based, multi-label (annotation followed by metastasis association), whole slide image analysis pipeline (n=90) that identified a PanNET high risk group with over an 8x higher risk of metastasis (versus the low risk group p\u3c0.05), regardless of cofounding clinical variables. These machine-learning based models may guide treatment decisions and demonstrate proof-of-principle that computational pathology has tremendous clinical utility