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Found 6 papers, 2 papers with code
Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge
Here, we present a DL model for aneurysm detection that overcomes the issue with ''weak'' labels: oversized annotations which are considerably faster to create.
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients.
An anatomically-informed 3D CNN for brain aneurysm classification with weak labels
We compare two strategies for negative patch sampling that have an increasing level of difficulty for the network and we show how this choice can strongly affect the results.
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware.
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection
The weak labels extracted from radiology reports allowed us to increase dataset size more than 3-fold, and improve VGG classification results from 75% to 82% AUC.
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox).
