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Found 17 papers, 6 papers with code
A generalized framework to predict continuous scores from medical ordinal labels
These labels are used to train and evaluate disease severity prediction models.
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.
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.
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.
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.
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.
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).
Deep Tone Mapping Operator for High Dynamic Range Images
In this paper, we address this problem by proposing a fast, parameter-free and scene-adaptable deep tone mapping operator (DeepTMO) that yields a high-resolution and high-subjective quality tone mapped output.
Unsupervised Representation Learning by Predicting Image Rotations
However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale.
Ranked #126 on
Self-Supervised Image Classification
on ImageNet
