Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival

Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers

Multimodal non-linear latent semantic method for information retrieval

La búsqueda y recuperación de datos multimodales es una importante tarea dentro del campo de búsqueda y recuperación de información, donde las consultas y los elementos de la base de datos objetivo están representados por un conjunto de modalidades, donde cada una de ellas captura un aspecto de un fenómeno de interés. Cada modalidad contiene información complementaria y común a otras modalidades. Con el fin de tomar ventaja de la información adicional distribuida a través de las distintas modalidades han sido desarrollados muchos algoritmos y métodos que utilizan las propiedades estadísticas en los datos multimodales para encontrar correlaciones implícitas, otros aprenden a calcular distancias heterogéneas, otros métodos aprenden a proyectar los datos desde el espacio de entrada hasta un espacio semántico común, donde las diferentes modalidades son comparables y se puede construir un ranking a partir de ellas. En esta tesis se presenta el diseño de un sistema para la búsqueda y recuperación de información multimodal que aprende varias proyecciones no lineales a espacios semánticos latentes donde las distintas modalidades son representadas en conjunto y es posible realizar comparaciones y medidas de similitud para construir rankings multimodales. Adicionalmente se propone un método kernelizado para la proyección de datos a un espacio semántico latente usando la información de las etiquetas como método de supervisión para construir índice multimodal que integra los datos multimodales y la información de las etiquetas; este método puede proyectar los datos a tres diferentes espacios semánticos donde varias configuraciones de búsqueda y recuperación de información pueden ser aplicadas. El sistema y el método propuestos fueron evaluados en un conjunto de datos compuesto por casos médicos, donde cada caso consta de una imagen de tejido prostático, un reporte de texto del patólogo y un valor de Gleason score como etiqueta de supervisión. Combinando la información multimodal y la información en las etiquetas se generó un índice multimodal que se utilizó para realizar la tarea de búsqueda y recuperación de información por contenido obteniendo resultados sobresalientes. Las proyecciones no-lineales permiten al modelo una mayor flexibilidad y capacidad de representación. Sin embargo calcular estas proyecciones no-lineales en un conjunto de datos enorme es computacionalmente costoso, para reducir este costo y habilitar el modelo para procesar datos a gran escala, la técnica del budget fue utilizada, mostrando un buen compromiso entre efectividad y velocidad.Multimodal information retrieval is an information retrieval sub-task where queries and database target elements are composed of several modalities or views. A modality is a representation of complex phenomena, captured and measured by different sensors or information sources, each one encodes some information about it. Each modality representation contains complementary and shared information about the phenomenon of interest, this additional information can be used to improve the information retrieval process. Several methods have been developed to take advantage of additional information distributed across different modalities. Some of them exploit statistical properties in multimodal data to find correlations and implicit relationships, others learn heterogeneous distance functions, and others learn linear and non-linear projections that transform data from the original input space to a common latent semantic space where different modalities are comparable. In spite of the attention dedicated to this issue, multimodal information retrieval is still an open problem. This thesis presents a multimodal information retrieval system designed to learn several mapping functions to transform multimodal data to a latent semantic space, where different modalities are combined and can be compared to build a multimodal ranking and perform a multimodal information retrieval task. Additionally, a multimodal kernelized latent semantic embedding method is proposed to construct a supervised multimodal index, integrating multimodal data and label supervision. This method can perform mappings to three different spaces where some information retrieval task setups can be performed. The proposed system and method were evaluated in a multimodal medical case-based retrieval task where data is composed of whole-slide images of prostate tissue samples, pathologist’s text report and Gleason score as a supervised label. Multimodal data and labels were combined to produce a multimodal index. This index was used to retrieve multimodal information and achieves outstanding results compared with previous works on this topic. Non-linear mappings provide more flexibility and representation capacity to the proposed model. However, constructing the non-linear mapping in a large dataset using kernel methods can be computationally costly. To reduce the cost and allow large scale applications, the budget technique was introduced, showing good performance between speed and effectiveness.COLCIENCIASJóvenes investigadores 761/2016Línea de investigación: Ciencias de la computaciónMaestrí

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

WWFedCBMIR:World-Wide Federated Content-Based Medical Image Retrieval

The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set. FedCBMIR distributes an unsupervised FE to collaborative centers for training without sharing the data set, resulting in shorter training times and higher performance. FedCBMIR was evaluated by mimicking two experiments, including two clients with two different breast cancer data sets, namely BreaKHis and Camelyon17 (CAM17), and four clients with the BreaKHis data set at four different magnifications. FedCBMIR increases the F1 score (F1S) of each client from 96% to 98.1% in CAM17 and from 95% to 98.4% in BreaKHis, with 11.44 fewer hours in training time. FedCBMIR provides 98%, 96%, 94%, and 97% F1S in the BreaKHis experiment with a generalized model and accomplishes this in 25.53 fewer hours of training