Magnetic Particle Imaging (MPI) is an innovative medical modality, providing nanomolar-scale in vivo sensitivity and radiation-free dynamic real-time detection for precision medicine. However, MPI faces a challenging problem in accurately visualizing nanoparticle distributions, where the reconstructed images with unidirectional scanning exhibit anisotropy. The anisotropy in spatial resolution leads to distortion and blurred image boundaries. Existing deep learning methods for anisotropy calibration are only limited to simulation data due to lacking of real-world MPI datasets. To address the aforementioned problems, we spent over three years designing and constructing a real-world MPI anisotropic image datasets (20,156 images) with diverse phantoms (sensitivity, resolution, vessel, shape) and animal scanning. Then, we introduce a novel Mamba-based method, MPI-Mamba, for anisotropic image calibration. Specifically, we propose a latent feature fusion state space model (LFF-SSM) block for feature fusion and leverage conditional latent diffusion model (CL-DM) branch for feature extraction. The CL-DM is performed to extract latent features in a highly compressed latent space for guiding the calibration and deblurring process. Next, we exploit the LFF-SSM to fully fuse the extracted multi-scale features to capture contextual information from the image structure, enabling the model to learn the overall distribution of signal concentration. We evaluate our method and competing methods on simulation dataset and our constructed diverse real-world MPI datasets. The results show that our proposed approach outperforms competing methods for anisotropic image calibration and deblurring.

In software crowdsourcing, task prize is a primary incentive for engaging crowd developers. One of the main challenges in crowdsourcing task pricing is to determine appropriate prizes in order to attract qualified workers. Few studies proposed methods to address this challenge. However, they are either too theoretical or too restricted to be applied for early crowdsourcing planning. In this study, we propose a novel approach, i.e., PTMA, to support early task pricing in software crowdsourcing from textual task requirements. PTMA consists of three phases, namely data pre‐processing, topic extraction, and topic‐based task pricing analysis, integrating 6 machine learning algorithms and 3 analogy‐based models for topic‐based pricing analysis. PTMA is evaluated using data from 2016 software crowdsourcing tasks extracted from TopCoder, the largest software crowdsourcing platform. The results show that: 1) textual requirement information can aid early task pricing in software crowdsourcing; 2) the best predictor in PTMA, based on logistic regression, achieves an accuracy of 88.3% in Pred (30); and 3) PTMA outperforms the existing baseline models by 9% in Pred (30). PTMA greatly simplifies the pricing process by only leveraging textual task description as inputs, and can achieve better prediction accuracy in making task pricing decisions.

Modern e-health systems have undergone rapid development thanks to the advances in communications, computing and machine learning technology. Especially, deep learning has great superiority in image analysis and disease prediction. In this paper, we use Alzheimer's Disease (AD) as an example to show advantages of deep learning in diagnosing brain diseases and providing clinical decision support. Firstly, we convert raw functional magnetic resonance imaging (fMRI) to a matrix to represent activity of 90 brain regions. Secondly, to represent the functional connectivity between different brain regions, a correlation matrix is obtained by calculating the correlation between each pair of brain regions. In the next, a targeted autoencoder network is built to classify the correlation matrix, which is sensitive to AD. Finally, the experiment results show that our proposed method for AD prediction achieves much better effects than traditional means. It finds the correlations between different brain regions efficiently, provides strong reference for AD prediction. Compared to Support Vector Machine (SVM), about 25% improvement is gained in prediction accuracy. The e-health field becomes more complete and effective owing to that. Our work helps predict AD at an early stage and take measures to slow down or even prevent the onset of it.