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02/15/2022
Improving the repeatability of deep learning models with Monte Carlo dropout
The integration of artificial intelligence into clinical workflows requi...
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02/15/2022
Label fusion and training methods for reliable representation of inter-rater uncertainty
Medical tasks are prone to inter-rater variability due to multiple facto...
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11/12/2021
Monte Carlo dropout increases model repeatability
The integration of artificial intelligence into clinical workflows requi...
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09/12/2021
Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge
This paper gives a detailed description of the pipelines used for the 2n...
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09/09/2021
Fair Conformal Predictors for Applications in Medical Imaging
Deep learning has the potential to augment many components of the clinic...
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07/06/2021
Evaluating subgroup disparity using epistemic uncertainty in mammography
As machine learning (ML) continue to be integrated into healthcare syste...
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02/18/2021
Benefits of Linear Conditioning for Segmentation using Metadata
Medical images are often accompanied by metadata describing the image (v...
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12/23/2020
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
Spinal cord tumors lead to neurological morbidity and mortality. Being a...
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11/18/2020
SoftSeg: Advantages of soft versus binary training for image segmentation
Most image segmentation algorithms are trained on binary masks formulate...
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10/20/2020
ivadomed: A Medical Imaging Deep Learning Toolbox
ivadomed is an open-source Python package for designing, end-to-end trai...
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10/03/2019
