Machine Learning Methods for Medical and Biological Image Computing

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries in neuroscience. In this thesis, I propose computational methods using high-level features for automated analysis of brain images at different levels. At the brain function level, I develop a deep learning based framework for completing and integrating multi-modality neuroimaging data, which increases the diagnosis accuracy for Alzheimer’s disease. At the cellular level, I propose to use three dimensional convolutional neural networks (CNNs) for segmenting the volumetric neuronal images, which improves the performance of digital reconstruction of neuron structures. I design a novel CNN architecture such that the model training and testing image prediction can be implemented in an end-to-end manner. At the molecular level, I build a voxel CNN classifier to capture discriminative features of the input along three spatial dimensions, which facilitate the identification of secondary structures of proteins from electron microscopy im-ages. In order to classify genes specifically expressed in different brain cell-type, I propose to use invariant image feature descriptors to capture local gene expression information from cellular-resolution in situ hybridization images. I build image-level representations by applying regularized learning and vector quantization on generated image descriptors. The developed computational methods in this dissertation are evaluated using images from medical and biological experiments in comparison with baseline methods. Experimental results demonstrate that the developed representations, formulations, and algorithms are effective and efficient in learning from brain imaging data

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

Diseño de red neuronal 1D para detectar arritmias cardiacas

This article shows a neuronal network for deep learning focused on recognizing and classification five types of cardiac signals (Sinus, Ventricular Tachycardia, Ventricular Fibrillation, Atrial Flutter, and Atrial Fibrillation). The final objective is to obtain an architecture that can be implemented in an embedded system as a pre-diagnostic device linked to a Holter monitoring system. The network was designed using the Keras API programmed in Python, where it is possible to obtain a comparison of different types of networks that vary the presence of a residual block, with the result that the network with said block obtains the best response (100% success rate) and a model loss of approximately 0.15%. On the other hand, a validation by means of confusion matrices was carried out to verify the existence of false positives in the network results and evidence what type of arrhythmia can be presented according to the network output against an input signal through the console.El presente artículo muestra el diseño de una red neuronal para aprendizaje profundo enfocado al reconocimiento y clasificación de cinco tipos de señales cardiacas (Sinusal, taquicardia ventricular, fibrilación ventricular, flutter atrial y fibrilación atrial). El objetivo es obtener una arquitectura que pueda ser implementada en un sistema embebido como un dispositivo de prediagnóstico que se pueda vincular a un sistema de monitorización de un holter. La red fue diseñada por medio de la API de Keras programada en Python, en donde se logra obtener una comparación de diferentes tipos de redes que varían la presencia de un bloque residual, teniendo como resultado que la red con dicho bloque obtiene la mejor respuesta (porcentaje de aciertos de 100%) y una pérdida del modelo aproximadamente del 0.15%. Por otro lado, se realizó una validación mediante matrices de confusión para verificar la existencia de falsos positivos en los resultados otorgados por la red y adicionalmente evidenciar que tipo de arritmia se puede presentar conforme la salida de la red frente a una señal de entrada por medio de la consola