866 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Neuroconductor: an R platform for medical imaging analysis
Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience
Medical imaging analysis: Automatic hippocampus segmentation
Alzheimer’s disease (AD) is a major cause of disability in the developed countries and places. The objective of our research is to increase the likelihood of early recognition and assessment of Alzheimer Disease so that concern can be eliminated if it is not warranted; treatable conditions can be identified and addressed appropriately; and non-reversible conditions can be diagnosed early enough to permit the patient and family to plan for contingencies such as long-term care. We developed computational tools for the automatic analysis of Medial Temporal Lobe atrophy starting from large sets of structural MR images and we are providing an IT infrastructure built on a high available, high scalable computing cluster, integrated with our neuroimages analysis tools
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical
Imaging (MLMI 2017
Self-supervised learning methods and applications in medical imaging analysis: A survey
The scarcity of high-quality annotated medical imaging datasets is a major
problem that collides with machine learning applications in the field of
medical imaging analysis and impedes its advancement. Self-supervised learning
is a recent training paradigm that enables learning robust representations
without the need for human annotation which can be considered an effective
solution for the scarcity of annotated medical data. This article reviews the
state-of-the-art research directions in self-supervised learning approaches for
image data with a concentration on their applications in the field of medical
imaging analysis. The article covers a set of the most recent self-supervised
learning methods from the computer vision field as they are applicable to the
medical imaging analysis and categorize them as predictive, generative, and
contrastive approaches. Moreover, the article covers 40 of the most recent
research papers in the field of self-supervised learning in medical imaging
analysis aiming at shedding the light on the recent innovation in the field.
Finally, the article concludes with possible future research directions in the
field
Novel Deep Learning Models for Medical Imaging Analysis
abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
Multi-Modal Medical Imaging Analysis with Modern Neural Networks
Medical imaging is an important non-invasive tool for diagnostic and treatment purposes in medical practice. However, interpreting medical images is a time consuming and challenging task. Computer-aided diagnosis (CAD) tools have been used in clinical practice to assist medical practitioners in medical imaging analysis since the 1990s. Most of the current generation of CADs are built on conventional computer vision techniques, such as manually defined feature descriptors. Deep convolutional neural networks (CNNs) provide robust end-to-end methods that can automatically learn feature representations. CNNs are a promising building block of next-generation CADs. However, applying CNNs to medical imaging analysis tasks is challenging. This dissertation addresses three major issues that obstruct utilizing modern deep neural networks on medical image analysis tasks---lack of domain knowledge in architecture design, lack of labeled data in model training, and lack of uncertainty estimation in deep neural networks. We evaluated the proposed methods on six large, clinically-relevant datasets. The result shows that the proposed methods can significantly improve the deep neural network performance on medical imaging analysis tasks
Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis
Despite the rapid progress in self-supervised learning (SSL), end-to-end
fine-tuning still remains the dominant fine-tuning strategy for medical imaging
analysis. However, it remains unclear whether this approach is truly optimal
for effectively utilizing the pre-trained knowledge, especially considering the
diverse categories of SSL that capture different types of features. In this
paper, we first establish strong contrastive and restorative SSL baselines that
outperform SOTA methods across four diverse downstream tasks. Building upon
these strong baselines, we conduct an extensive fine-tuning analysis across
multiple pre-training and fine-tuning datasets, as well as various fine-tuning
dataset sizes. Contrary to the conventional wisdom of fine-tuning only the last
few layers of a pre-trained network, we show that fine-tuning intermediate
layers is more effective, with fine-tuning the second quarter (25-50%) of the
network being optimal for contrastive SSL whereas fine-tuning the third quarter
(50-75%) of the network being optimal for restorative SSL. Compared to the
de-facto standard of end-to-end fine-tuning, our best fine-tuning strategy,
which fine-tunes a shallower network consisting of the first three quarters
(0-75%) of the pre-trained network, yields improvements of as much as 5.48%.
Additionally, using these insights, we propose a simple yet effective method to
leverage the complementary strengths of multiple SSL models, resulting in
enhancements of up to 3.57% compared to using the best model alone. Hence, our
fine-tuning strategies not only enhance the performance of individual SSL
models, but also enable effective utilization of the complementary strengths
offered by multiple SSL models, leading to significant improvements in
self-supervised medical imaging analysis
Sharing images: The why and how of medical imaging analysis research
Presentation on Sharing images: The why and how of medical imaging analysis research for the Health-RI infrastructure for the Health-RI FAIR data stewards basics course in Utrecht, on 3 July 202
Hypothesis testing for medical imaging analysis via the smooth Euler characteristic transform
Shape-valued data are of interest in applied sciences, particularly in
medical imaging. In this paper, inspired by a specific medical imaging example,
we introduce a hypothesis testing method via the smooth Euler characteristic
transform to detect significant differences among collections of shapes. Our
proposed method has a solid mathematical foundation and is computationally
efficient. Through simulation studies, we illustrate the performance of our
proposed method. We apply our method to images of lung cancer tumors from the
National Lung Screening Trial database, comparing its performance to a
state-of-the-art machine learning model
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