The increasing literature leads to formidable pressure for medical researchers. Most existing recommender approaches mainly depend on text-based information. How to extract and utilize the heterogeneous information, especially the graphic ones, to improve the recommender is worthy of further exploring. To this end, we establish a document-to-document recommender system for medical literature (D2D-MR). Specifically, we proposed HB-GED, the Half-branch GED algorithm, and the bipartite-graph-based algorithm for solving the molecule similarity and the paper similarity, respectively. Experimental results on real-world datasets demonstrate the effectiveness of the proposed recommender system.
CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model’s performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.
MR images scanned at low magnetic field ($<1$T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those from high field (typically 1.5T and 3T). We adapt the recent idea of Image Quality Transfer (IQT) to enhance very low-field structural images aiming to estimate the resolution, spatial coverage, and contrast of high-field images. Analogous to many learning-based image enhancement techniques, IQT generates training data from high-field scans alone by simulating low-field images through a pre-defined decimation model. However, the ground truth decimation model is not well-known in practice, and lack of its specification can bias the trained model, aggravating performance on the real low-field scans. In this paper we propose a probabilistic decimation simulator to improve robustness of model training. It is used to generate and augment various low-field images whose parameters are random variables and sampled from an empirical distribution related to tissue-specific SNR on a 0.36T scanner. The probabilistic decimation simulator is model-agnostic, that is, it can be used with any super-resolution networks. Furthermore we propose a variant of U-Net architecture to improve its learning performance. We show promising qualitative results from clinical low-field images confirming the strong efficacy of IQT in an important new application area: epilepsy diagnosis in sub-Saharan Africa where only low-field scanners are normally available.
Numerical wavefield simulation such as commercial simulation software enables an optimal design of an ultrasound computed tomography (USCT) system for clinical purpose of before prototyping. Such simulator, not developed for optimal design though, can provide rapid implementation for acoustic wave propagation but may lead to unexpected errors during the establishment of numerical model. Here, we propose an auto-tuning numerical method (ATNM), aiming to optimize physical parameters (e.g. grid size, Courant-Friedrichs-Lewy (CFL) number, perfectly matched layer (PML) absorption coefficient, etc) such that the enumerated wavefield computed on those converges to the corresponding analytical solution derived from acoustic scattering theory. We use genetic algorithm (GA) to automatically calibrate numerical wavefield. Our preliminary test is to investigate the best design of PML absorption coefficient for USCT to minimize mean relative error (MRE) between the k-Wave simulation and the analytic model and show its efficacy. The experimental results verify our hypothesis that this calibrated numerical simulator on a simple physical domain is generalizable to any other domains.
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