87,381 research outputs found
Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis
Transfer learning represents a recent paradigm shift in the way we build
artificial intelligence (AI) systems. In contrast to training task-specific
models, transfer learning involves pre-training deep learning models on a large
corpus of data and minimally fine-tuning them for adaptation to specific tasks.
Even so, for 3D medical imaging tasks, we do not know if it is best to
pre-train models on natural images, medical images, or even synthetically
generated MRI scans or video data. To evaluate these alternatives, here we
benchmarked vision transformers (ViTs) and convolutional neural networks
(CNNs), initialized with varied upstream pre-training approaches. These methods
were then adapted to three unique downstream neuroimaging tasks with a range of
difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD)
classification, "brain age" prediction. Experimental tests led to the following
key observations: 1. Pre-training improved performance across all tasks
including a boost of 7.4% for AD classification and 4.6% for PD classification
for the ViT and 19.1% for PD classification and reduction in brain age
prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video
or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in
limited-data settings, and in-domain pretraining enhanced their performances,
4. Pre-training improved generalization to out-of-distribution datasets and
sites. Overall, we benchmarked different vision architectures, revealing the
value of pre-training them with emerging datasets for model initialization. The
resulting pre-trained models can be adapted to a range of downstream
neuroimaging tasks, even when training data for the target task is limited
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
Brain-mediated Transfer Learning of Convolutional Neural Networks
The human brain can effectively learn a new task from a small number of
samples, which indicate that the brain can transfer its prior knowledge to
solve tasks in different domains. This function is analogous to transfer
learning (TL) in the field of machine learning. TL uses a well-trained feature
space in a specific task domain to improve performance in new tasks with
insufficient training data. TL with rich feature representations, such as
features of convolutional neural networks (CNNs), shows high generalization
ability across different task domains. However, such TL is still insufficient
in making machine learning attain generalization ability comparable to that of
the human brain. To examine if the internal representation of the brain could
be used to achieve more efficient TL, we introduce a method for TL mediated by
human brains. Our method transforms feature representations of audiovisual
inputs in CNNs into those in activation patterns of individual brains via their
association learned ahead using measured brain responses. Then, to estimate
labels reflecting human cognition and behavior induced by the audiovisual
inputs, the transformed representations are used for TL. We demonstrate that
our brain-mediated TL (BTL) shows higher performance in the label estimation
than the standard TL. In addition, we illustrate that the estimations mediated
by different brains vary from brain to brain, and the variability reflects the
individual variability in perception. Thus, our BTL provides a framework to
improve the generalization ability of machine-learning feature representations
and enable machine learning to estimate human-like cognition and behavior,
including individual variability
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
Disease Knowledge Transfer across Neurodegenerative Diseases
We introduce Disease Knowledge Transfer (DKT), a novel technique for
transferring biomarker information between related neurodegenerative diseases.
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative
diseases even when only limited, unimodal data is available, by transferring
information from larger multimodal datasets from common neurodegenerative
diseases. DKT is a joint-disease generative model of biomarker progressions,
which exploits biomarker relationships that are shared across diseases. Our
proposed method allows, for the first time, the estimation of plausible,
multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare
neurodegenerative disease where only unimodal MRI data is available. For this
we train DKT on a combined dataset containing subjects with two distinct
diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD)
dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior
Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for
which only a limited number of Magnetic Resonance Imaging (MRI) scans are
available. Although validation is challenging due to lack of data in PCA, we
validate DKT on synthetic data and two patient datasets (TADPOLE and PCA
cohorts), showing it can estimate the ground truth parameters in the simulation
and predict unseen biomarkers on the two patient datasets. While we
demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other
forms of related neurodegenerative diseases. Source code for DKT is available
online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table
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