Machine learning in medical imaging. MLMI (Workshop)最新文献

The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.

This paper proposes a framework of fast neuroimaging-based retrieval and AD analysis, by three key steps: (1) landmark detection, which efficiently extracts landmark-based neuroimaging features without the need of nonlinear registration in testing stage; (2) landmark selection, which removes redundant/noisy landmarks via proposing a feature selection method that considers structural information among landmarks; and (3) hashing, which converts high-dimensional features of subjects into binary codes, for efficiently conducting approximate nearest neighbor search and diagnosis of AD. We have conducted experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and demonstrated that our framework could achieve higher performance than the comparison methods, in terms of accuracy and speed (at least 100 times faster).

Neuroimaging data have been widely used to derive possible biomarkers for Alzheimer's Disease (AD) diagnosis. As only certain brain regions are related to AD progression, many feature selection methods have been proposed to identify informative features (i.e., brain regions) to build an accurate prediction model. These methods mostly only focus on the feature-target relationship to select features which are discriminative to the targets (e.g., diagnosis labels). However, since the brain regions are anatomically and functionally connected, there could be useful intrinsic relationships among features. In this paper, by utilizing both the feature-target and feature-feature relationships, we propose a novel sparse regression model to select informative features which are discriminative to the targets and also representative to the features. We argue that the features which are representative (i.e., can be used to represent many other features) are important, as they signify strong "connection" with other ROIs, and could be related to the disease progression. We use our model to select features for both binary and multi-class classification tasks, and the experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the proposed method outperforms other comparison methods considered in this work.

Multi-atlas based morphometric pattern analysis has been recently proposed for the automatic diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI), where multi-view feature representations for subjects are generated by using multiple atlases. However, existing multi-atlas based methods usually assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while the underlying distribution of data is actually a prior unknown. In this paper, we propose an inherent structure-guided multi-view leaning (ISML) method for AD/MCI classification. Specifically, we first extract multi-view features for subjects using multiple selected atlases, and then cluster subjects in the original classes into several sub-classes (i.e., clusters) in each atlas space. Then, we encode each subject with a new label vector, by considering both the original class labels and the coding vectors for those sub-classes, followed by a multi-task feature selection model in each of multi-atlas spaces. Finally, we learn multiple SVM classifiers based on the selected features, and fuse them together by an ensemble classification method. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves better performance than several state-of-the-art methods in AD/MCI classification.

Segmenting T2-hyperintense white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the T₁-w and FLAIR scans of all time-points. For every 4D patch from a subject, a few relevant matching 4D patches are found from a reference, such that their convex combination reconstructs the subject's 4D patch. Then corresponding manual segmentation patches of the reference are combined in a similar manner to generate a 4D membership of lesions of the subject patch. We compare our 4D patch-based segmentation with independent 3D voxel-based and patch-based lesion segmentation algorithms. Based on ground truth segmentations from 30 data sets, we show that the mean Dice coefficients between manual and automated segmentations improve after using the 4D approach compared to two state-of-the-art 3D segmentation algorithms.

Random forest has been widely recognized as one of the most powerful learning-based predictors in literature, with a broad range of applications in medical imaging. Notable efforts have been focused on enhancing the algorithm in multiple facets. In this paper, we present an original concept of multi-source information gain that escapes from the conventional notion inherent to random forest. We propose the idea of characterizing information gain in the training process by utilizing multiple beneficial sources of information, instead of the sole governing of prediction targets as conventionally known. We suggest the use of location and input image patches as the secondary sources of information for guiding the splitting process in random forest, and experiment on the challenging task of predicting CT images from MRI data. The experimentation is thoroughly analyzed in two datasets, i.e., human brain and prostate, with its performance further validated with the integration of auto-context model. Results prove that the multi-source information gain concept effectively helps better guide the training process with consistent improvement in prediction accuracy.

Random Forest (RF) has been widely used in the learning-based labeling. In RF, each sample is directed from the root to each leaf based on the decisions made in the interior nodes, also called splitting nodes. The splitting nodes assign a testing sample to either left or right child based on the learned splitting function. The final prediction is determined as the average of label probability distributions stored in all arrived leaf nodes. For ambiguous testing samples, which often lie near the splitting boundaries, the conventional splitting function, also referred to as hard split function, tends to make wrong assignments, hence leading to wrong predictions. To overcome this limitation, we propose a novel soft-split random forest (SSRF) framework to improve the reliability of node splitting and finally the accuracy of classification. Specifically, a soft split function is employed to assign a testing sample into both left and right child nodes with their certain probabilities, which can effectively reduce influence of the wrong node assignment on the prediction accuracy. As a result, each testing sample can arrive at multiple leaf nodes, and their respective results can be fused to obtain the final prediction according to the weights accumulated along the path from the root node to each leaf node. Besides, considering the importance of context information, we also adopt a Haar-features based context model to iteratively refine the classification map. We have comprehensively evaluated our method on two public datasets, respectively, for labeling hippocampus in MR images and also labeling three organs in Head & Neck CT images. Compared with the hard-split RF (HSRF), our method achieved a notable improvement in labeling accuracy.

In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer's Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis. The purpose of mapping HOG features onto the space of ROI features is to provide complementary information so that features from different views can not only be comparable (i.e., homogeneous) but also be interpretable. For example, ROI features are robust to noise, but lack of reflecting small or subtle changes, while HOG features are diverse but less robust to noise. The proposed multi-view learning method is designed to learn the transformation between two spaces and to separate the classes under the supervision of class labels. The experimental results on the MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the proposed multi-view method helps enhance disease status identification performance, outperforming both baseline methods and state-of-the-art methods.