An algorithm for learning shape and appearance models without annotations.

08:00 EDT 30th April 2019 | BioPortfolio

Summary of "An algorithm for learning shape and appearance models without annotations."

This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to the MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle "missing data", which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.


Journal Details

This article was published in the following journal.

Name: Medical image analysis
ISSN: 1361-8423
Pages: 197-215


DeepDyve research library

PubMed Articles [12719 Associated PubMed Articles listed on BioPortfolio]

Neural network models and deep learning.

Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence. They can approximate functions and dynamics by learning fro...

Cooperative Learning of Descriptor and Generator Networks.

This paper studies the cooperative learning of two generative models. Both models are parametrized by ConvNets. The first model is a deep energy-based model, whose energy function is defined by a bott...

Learning Algorithm for Boltzmann Machines With Additive Weight and Bias Noise.

This brief presents analytical results on the effect of additive weight/bias noise on a Boltzmann machine (BM), in which the unit output is in {-1, 1} instead of {0,1}. With such noise, it is found th...

Genetic algorithm for assigning weights to gene expressions using functional annotations.

A method, named genetic algorithm for assigning weights to gene expressions using functional annotations (GAAWGEFA), is developed to assign proper weights to the gene expressions at each time point. T...

Automatic Pathological Lung Segmentation in Low-dose CT Image using Eigenspace Sparse Shape Composition.

Segmentation of lungs with severe pathology is a nontrivial problem in clinical application. Due to complex structures, pathological changes, individual differences and low image quality, accurate lun...

Clinical Trials [3450 Associated Clinical Trials listed on BioPortfolio]

Change in Penile Length Following Bilateral Nerve-Sparing Radical Prostatectomy

Some men complain of changes in the shape or dimensions of their penis after undergoing radical prostatectomy (removal of the prostate) for prostate cancer. Changes in penile dimensions in...

Physiological Validation of Current Machine Learning Models for Hemodynamic Instability in Humans

This study will be collecting data on participants undergoing lower body negative pressure (LBNP) to simulate progressive blood loss. The goal of the study is to collect data to allow for ...

Randomized Controlled Trial of a Machine Learning Algorithm for Early Sepsis Detection

The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Ce...

Free Text Prediction Algorithm for Appendicitis

Computer-aided diagnostic software has been used to assist physicians in various ways. Text-based prediction algorithms have been trained on past medical records through data mining and fe...

Shape Up! Adults Study

Identify the unique associations of body shape to body composition indices in a population that represents the variance of sex, age, BMI, and ethnicity found in the US population. Describ...

Medical and Biotech [MESH] Definitions

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)

Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.

Usually refers to the use of mathematical models in the prediction of learning to perform tasks based on the theory of probability applied to responses; it may also refer to the frequency of occurrence of the responses observed in the particular study.

Theoretical models which propose methods of learning or teaching as a basis or adjunct to changes in attitude or behavior. These educational interventions are usually applied in the fields of health and patient education but are not restricted to patient care.

Quick Search


DeepDyve research library

Searches Linking to this Article