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Benchmarking a Benchmark: How Reliable is MS-COCO?
Benchmark datasets are used to profile and compare algorithms across a variety of tasks, ranging from image classification to segmentation, and also play a large role in image pretraining algorithms.
An Empirical Study of Uncertainty in Polygon Annotation and the Impact of Quality Assurance
Polygons are a common annotation format used for quickly annotating objects in instance segmentation tasks.
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.
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.
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.
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.
