579 research outputs found
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation
(SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of
automatically identifying pathologies in brain images. Our work challenges the
effectiveness of current Machine Learning (ML) approaches in this application
domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR)
MR scans provides better anomaly segmentation maps than several different
ML-based anomaly detection models. Specifically, our method achieves better
Dice similarity coefficients and Precision-Recall curves than the competitors
on various popular evaluation data sets for the segmentation of tumors and
multiple sclerosis lesions.Comment: 10 pages, 4 figures, accepted to the MICCAI 2021 BrainLes Worksho
A Review of Causality for Learning Algorithms in Medical Image Analysis
Medical image analysis is a vibrant research area that offers doctors and
medical practitioners invaluable insight and the ability to accurately diagnose
and monitor disease. Machine learning provides an additional boost for this
area. However, machine learning for medical image analysis is particularly
vulnerable to natural biases like domain shifts that affect algorithmic
performance and robustness. In this paper we analyze machine learning for
medical image analysis within the framework of Technology Readiness Levels and
review how causal analysis methods can fill a gap when creating robust and
adaptable medical image analysis algorithms. We review methods using causality
in medical imaging AI/ML and find that causal analysis has the potential to
mitigate critical problems for clinical translation but that uptake and
clinical downstream research has been limited so far.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA)
https://www.melba-journal.org/papers/2022:028.html". ; Paper ID: 2022:02
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
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