TL;DR
UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data, and potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed.
Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tom...
This work investigates two complementary methods to address CT scan analysis challenges: multi-instance learning (MIL) and anatomical filtering, and shows that anatomical filtering with the Compass model is critical for the out-of-distribution generalization of embedding-based ABMIL.
This survey extends beyond traditional and deep learning-based augmentation techniques or deep semi-supervised approaches, by explicitly focusing on medical/clinical imaging modalities, by explicitly focusing on CT, MRI, and X-ray, offering a broader perspective.
A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.
For small-sample medical image classification, this work recommends cross-validation-based HPO when computational resources permit because it trades additional computation for a more reliable development-time estimate of subsequent test performance.
A Systematic deep learning framework based on an enhanced ResNet50 architecture to improve brain tumor classification and surpass benchmark models such as VGG16, MobileNet, InceptionV3, and Xception is introduced.
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