1,414 research outputs found
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays
Real-world application of chest X-ray abnormality classification requires
dealing with several challenges: (i) limited training data; (ii) training and
evaluation sets that are derived from different domains; and (iii) classes that
appear during training may have partial overlap with classes of interest during
evaluation. To address these challenges, we present an integrated framework
called Generalized Cross-Domain Multi-Label Few-Shot Learning (GenCDML-FSL).
The framework supports overlap in classes during training and evaluation,
cross-domain transfer, adopts meta-learning to learn using few training
samples, and assumes each chest X-ray image is either normal or associated with
one or more abnormalities. Furthermore, we propose Generalized Episodic
Training (GenET), a training strategy that equips models to operate with
multiple challenges observed in the GenCDML-FSL scenario. Comparisons with
well-established methods such as transfer learning, hybrid transfer learning,
and multi-label meta-learning on multiple datasets show the superiority of our
approach.Comment: 17 page
Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks
In safety-critical applications like medical diagnosis, certainty associated
with a model's prediction is just as important as its accuracy. Consequently,
uncertainty estimation and reduction play a crucial role. Uncertainty in
predictions can be attributed to noise or randomness in data (aleatoric) and
incorrect model inferences (epistemic). While model uncertainty can be reduced
with more data or bigger models, aleatoric uncertainty is more intricate. This
work proposes a novel approach that interprets data uncertainty estimated from
a self-supervised task as noise inherent to the data and utilizes it to reduce
aleatoric uncertainty in another task related to the same dataset via data
augmentation. The proposed method was evaluated on a benchmark medical imaging
dataset with image reconstruction as the self-supervised task and segmentation
as the image analysis task. Our findings demonstrate the effectiveness of the
proposed approach in significantly reducing the aleatoric uncertainty in the
image segmentation task while achieving better or on-par performance compared
to the standard augmentation techniques.Comment: Accepted in IEEE International Symposium on Biomedical Imaging (ISBI)
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