AIMC Topic: Algorithms

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DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy.

Physics in medicine and biology
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation qua...

Towards Fine-Grained Human Pose Transfer With Detail Replenishing Network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for prod...

Machine learning predictive model for severe COVID-19.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji...

Machine Learning-Based Prediction of 6-Month Postoperative Karnofsky Performance Status in Patients with Glioblastoma: Capturing the Real-Life Interaction of Multiple Clinical and Oncologic Factors.

World neurosurgery
OBJECTIVE: Ability to thrive after invasive and intensive treatment is an important parameter to assess in patients with glioblastoma multiforme (GBM). Karnofsky Performance Status (KPS) is used to identify those patients suitable for postoperative r...

A deep learning approach to identify blepharoptosis by convolutional neural networks.

International journal of medical informatics
PURPOSE: Blepharoptosis is a known cause of reversible vision loss. Accurate assessment can be difficult, especially amongst non-specialists. Existing automated techniques disrupt clinical workflow by requiring user input, or placement of reference m...

Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions.

IEEE transactions on visualization and computer graphics
Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, le...

Cartographic Relief Shading with Neural Networks.

IEEE transactions on visualization and computer graphics
Shaded relief is an effective method for visualising terrain on topographic maps, especially when the direction of illumination is adapted locally to emphasise individual terrain features. However, digital shading algorithms are unable to fully match...

Visual Neural Decomposition to Explain Multivariate Data Sets.

IEEE transactions on visualization and computer graphics
Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a ...

HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks.

IEEE transactions on visualization and computer graphics
To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search...

Dense cellular segmentation for EM using 2D-3D neural network ensembles.

Scientific reports
Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has be...