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Found 8 papers, 0 papers with code
Responsible Deep Learning for Software as a Medical Device
Tools, models and statistical methods for signal processing and medical image analysis and training deep learning models to create research prototypes for eventual clinical applications are of special interest to the biomedical imaging community.
Reinforcement Learning in Non-Markovian Environments
Motivated by the novel paradigm developed by Van Roy and coauthors for reinforcement learning in arbitrary non-Markovian environments, we propose a related formulation and explicitly pin down the error caused by non-Markovianity of observations when the Q-learning algorithm is applied on this formulation.
Uncertainty Quantified Deep Learning for Predicting Dice Coefficient of Digital Histopathology Image Segmentation
Results from this study suggest that linear models can learn coefficients of uncertainty quantified deep learning and correlations ((Spearman's correlation (p<0. 05)) to predict Dice scores of specific regions of medical images.
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images
Deep learning (DL) models for disease classification or segmentation from medical images are increasingly trained using transfer learning (TL) from unrelated natural world images.
High Accuracy Tumor Diagnoses and Benchmarking of Hematoxylin and Eosin Stained Prostate Core Biopsy Images Generated by Explainable Deep Neural Networks
Our neural network framework thus is automated, explainable and performs high precision H&E staining and destaining of low cost native RGB images, and is computer vision and physician authenticated for rapid and accurate tumor diagnoses.
Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks
The staining model uses a conditional generative adversarial network that learns hierarchical non-linear mappings between whole slide RGB image (WSRI) pairs of prostate core biopsy before and after H&E staining.
Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health
We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease.
Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients
Imaging of microcirculatory dysfunction is a promising approach for automated diagnosis of sepsis.
