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Found 10 papers, 2 papers with code
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
However, models that are well calibrated overall can still be poorly calibrated for a sub-population, potentially resulting in a clinician unwittingly making poor decisions for this group based on the recommendations of the model.
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error.
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e. g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs.
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
This is particularly important in the context of medical image segmentation of pathological structures (e. g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others.
Heatmap Regression for Lesion Detection using Pointwise Annotations
In many clinical contexts, detecting all lesions is imperative for evaluating disease activity.
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process.
Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients.
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Many automatic machine learning models developed for focal pathology (e. g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts.
Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e. g. lesions, tumours) in patient images.
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images.
