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Found 37 papers, 15 papers with code
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data
Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
A generalized framework to predict continuous scores from medical ordinal labels
These labels are used to train and evaluate disease severity prediction models.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible.
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Estimating the test performance of software AI-based medical devices under distribution shifts is crucial for evaluating the safety, efficiency, and usability prior to clinical deployment.
Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes.
Improving the repeatability of deep learning models with Monte Carlo dropout
During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice.
Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating
Ranking by pairwise comparisons has shown improved reliability over ordinal classification.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Monte Carlo dropout increases model repeatability
Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.
Not Color Blind: AI Predicts Racial Identity from Black and White Retinal Vessel Segmentations
To determine whether RVM differences between Black and White eyes were physiological, CNNs were trained to predict race from color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs.
Deploying clinical machine learning? Consider the following...
Despite the intense attention and considerable investment into clinical machine learning research, relatively few applications have been deployed at a large-scale in a real-world clinical environment.
Fair Conformal Predictors for Applications in Medical Imaging
Deep learning has the potential to automate many clinically useful tasks in medical imaging.
Evaluating subgroup disparity using epistemic uncertainty in mammography
As machine learning (ML) continue to be integrated into healthcare systems that affect clinical decision making, new strategies will need to be incorporated in order to effectively detect and evaluate subgroup disparities to ensure accountability and generalizability in clinical workflows.
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning.
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology
The highdicom library ties into the extensive Python ecosystem for image processing and machine learning.
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types.
Towards Trainable Saliency Maps in Medical Imaging
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely limited by inherent black-box decision making and unsafe failure modes.
Federated Learning for Breast Density Classification: A Real-World Implementation
Building robust deep learning-based models requires large quantities of diverse training data.
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Saliency maps have become a widely used method to make deep learning models more interpretable by providing post-hoc explanations of classifiers through identification of the most pertinent areas of the input medical image.
Assessing the validity of saliency maps for abnormality localization in medical imaging
Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network.
Split Learning for collaborative deep learning in healthcare
Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up.
Give me (un)certainty -- An exploration of parameters that affect segmentation uncertainty
We assess the utility of three measures of uncertainty (Coefficient of Variation, Mean Pairwise Dice, and Mean Voxelwise Uncertainty) for the segmentation of a less ambiguous target structure (liver) and a more ambiguous one (liver tumors).
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al., 2018b).
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders.
Accelerated Experimental Design for Pairwise Comparisons
Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks.
Deep feature transfer between localization and segmentation tasks
In this paper, we propose a new pre-training scheme for U-net based image segmentation.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging.
High-resolution medical image synthesis using progressively grown generative adversarial networks
Additional convolutional layers are then iteratively introduced to produce images at twice the previous resolution until the desired resolution is reached.
Institutionally Distributed Deep Learning Networks
In such cases, sharing a deep learning model is a more attractive alternative.
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation
Deep learning has quickly become the weapon of choice for brain lesion segmentation.
Accelerated Reconstruction of Perfusion-Weighted MRI Enforcing Jointly Local and Nonlocal Spatio-temporal Constraints
Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique that allows one to measure tissue perfusion in an organ of interest through the injection of an intravascular paramagnetic contrast agent (CA).
