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Physics-informed Machine Learning for Medical Image Analysis
No full textThe incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). Integrating fundamental knowledge and governing physical laws not only improves analysis performance but also enhances the model’s robustness and interpretability. This work presents a systematic review of over 100 papers on the utility of PINNs dedicated to MIA (PIMIA) tasks. We propose a unified taxonomy to investigate what physics knowledge and processes are modeled, how they are represented, and the strategies to incorporate them into MIA models. We delve deep into a wide range of image analysis tasks, from imaging, generation, prediction, inverse imaging (super-resolution and reconstruction), registration, and image analysis (segmentation and classification). For each task, we thoroughly examine and present the central physics-guided operation, the region of interest (with respect to human anatomy), the corresponding imaging modality, the datasets used for model training, the deep network architectures employed, and the primary physical processes, equations, or principles utilized. Additionally, we also introduce a novel metric to compare the performance of PIMIA methods across different tasks and datasets. Based on this review, we summarize and distill our perspectives on the challenges, and highlight open research questions and directions for future research
Children's Engagement with Digital Technology in Educational Spaces: A Scoping Review
No full textChildren's Engagement with Digital Technology in Educational Spaces: A Scoping Revie
School Autonomy Reform and Social Justice in Australian Public Education
No full textSchool Autonomy Reform and Social Justice in Australian Public Educatio
Fragment-energy audio watermarking resilient to de-synchronization attacks
No full textFragment-energy audio watermarking resilient to de-synchronization attack
A comprehensive review on hybrid lattice meta-structures for biomedical engineering applications
No full textA comprehensive review on hybrid lattice meta-structures for biomedical engineering application
Efficient Constrained Real-World Problem-Solving Using Gradient-Based Mutation Manta Ray Foraging Optimization (GM-MRFO)
No full textThis paper presents a Gradient-based Mutation Manta ray foraging optimization (GM-MRFO) that is designed to solve real-world optimization problems with constraints. GM-MRFO combines the basic strategy of MRFO with the Gradient-based Mutation (GM) strategy, which is a feasibility-and-solution repair strategy adopted from the ϵ-Matrix-Adaptation Evolution Strategy (
MAgES). MRFO algorithm is not immune to the common problems confronted by constrained optimization algorithms where constraints in the optimization problem are incompatible, and a solution that satisfies all constraints does not exist. In such cases, the MRFO algorithm may not be able to find a feasible solution. Another challenge is the optimization algorithm converges to a solution that is not globally optimal. By introducing the GM strategy and using Jacobian approximation in finite differences, GM-MRFO can improve the feasibility of solutions throughout the search process, which enables it to handle constraints more effectively than its predecessor. The proposed algorithm's performance is evaluated by assessing the accuracy of the best solution produced, the feasibility rate, mean of violation, success rate, and the ranking based on a ranking scheme in the Congress on Evolutionary Computation 2020 (CEC2020). Specifically, GM-MRFO achieved a feasibility rate of 100% on 47 out of 57 CEC2020 real-world problems and improved adequately best-known solutions.</p
Cross-Model Denoising and Spearman-Based Negative Sample Filling for Implicit Feedback Recommendation
No full textCross-Model Denoising and Spearman-Based Negative Sample Filling for Implicit Feedback Recommendatio
Erratic grain rotation during annealing of a Mg alloy interpreted via shear-coupling
No full textErratic grain rotation during annealing of a Mg alloy interpreted via shear-couplin