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Monte Carlo Method

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Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB ("Boltzmann-Langevin One Body"), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate al...

Robosample: A rigid-body molecular simulation program based on robot mechanics.

Biochimica et biophysica acta. General subjects
BACKGROUND: Compared with all-atom molecular dynamics (MD), constrained MD methods allow for larger time steps, potentially reducing computational cost. For this reason, there has been continued interest in improving constrained MD algorithms to incr...

Understanding the learning mechanism of convolutional neural networks in spectral analysis.

Analytica chimica acta
Deep learning approaches, especially convolutional neural network (CNN) models, have achieved excellent performances in vibrational spectral analysis. The critical drawback of the CNN approach is the lack of interpretation, and it is regarded as a bl...

DeepDose: Towards a fast dose calculation engine for radiation therapy using deep learning.

Physics in medicine and biology
We present DeepDose, a deep learning framework for fast dose calculations in radiation therapy. Given a patient anatomy and linear-accelerator IMRT multi-leaf-collimator shape or segment, a novel set of physics-based inputs is calculated that encode ...

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

Medical physics
PURPOSE: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous...

A deep learning approach to radiation dose estimation.

Physics in medicine and biology
Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly, individual tissue density distributions as well as local variations of the concentration of the ra...

Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.

Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of distant metastasis. However, multi-class classification problems also have great clinical relevance, e.g...

Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...

Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflo...