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Found 12 papers, 1 papers with code
Multitask radiological modality invariant landmark localization using deep reinforcement learning
As a result, to develop a radiological decision support system, it would need to be equipped with potentially hundreds of deep learning models with each model trained for a specific task or organ.
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results
On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map.
MPRAD: A Multiparametric Radiomics Framework
The use of radiomics for quantitative extraction of textural features from radiological imaging is increasing moving towards clinical decision support.
Radiomic Synthesis Using Deep Convolutional Neural Networks
Radiomics is a rapidly growing field that deals with modeling the textural information present in the different tissues of interest for clinical decision support.
Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array
Results-The OncotypeDX classification by IRIS model had sensitivity of 95% and specificity of 89% with AUC of 0. 92.
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results
For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images.
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Therefore, we applied our multiparametric radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II and 16 grade IV).
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
A current clinical challenge is identifying limb girdle muscular dystrophy 2I(LGMD2I)tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle tissue.
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting.
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device.
Multi-environment lifelong deep reinforcement learning for medical imaging
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging.
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs.
