5,120 research outputs found
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
In recent years, several convolutional neural network (CNN) methods have been
proposed for the automated white matter lesion segmentation of multiple
sclerosis (MS) patient images, due to their superior performance compared with
those of other state-of-the-art methods. However, the accuracies of CNN methods
tend to decrease significantly when evaluated on different image domains
compared with those used for training, which demonstrates the lack of
adaptability of CNNs to unseen imaging data. In this study, we analyzed the
effect of intensity domain adaptation on our recently proposed CNN-based MS
lesion segmentation method. Given a source model trained on two public MS
datasets, we investigated the transferability of the CNN model when applied to
other MRI scanners and protocols, evaluating the minimum number of annotated
images needed from the new domain and the minimum number of layers needed to
re-train to obtain comparable accuracy. Our analysis comprised MS patient data
from both a clinical center and the public ISBI2015 challenge database, which
permitted us to compare the domain adaptation capability of our model to that
of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot
domain adaptation model trained using only a single image showed a performance
similar to that of other CNN methods that were fully trained using the entire
available training set, yielding a comparable human expert rater performance.
We believe that our experiments will encourage the MS community to incorporate
its use in different clinical settings with reduced amounts of annotated data.
This approach could be meaningful not only in terms of the accuracy in
delineating MS lesions but also in the related reductions in time and economic
costs derived from manual lesion labeling
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
Despite recent successes, the advances in Deep Learning have not yet been
fully translated to Computer Assisted Intervention (CAI) problems such as pose
estimation of surgical instruments. Currently, neural architectures for
classification and segmentation tasks are adopted ignoring significant
discrepancies between CAI and these tasks. We propose an automatic framework
(AutoSNAP) for instrument pose estimation problems, which discovers and learns
the architectures for neural networks. We introduce 1)~an efficient testing
environment for pose estimation, 2)~a powerful architecture representation
based on novel Symbolic Neural Architecture Patterns (SNAPs), and 3)~an
optimization of the architecture using an efficient search scheme. Using
AutoSNAP, we discover an improved architecture (SNAPNet) which outperforms both
the hand-engineered i3PosNet and the state-of-the-art architecture search
method DARTS.Comment: Accepted at MICCAI 2020 Preparing code for release at
https://github.com/MECLabTUDA/AutoSNA
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