12 research outputs found
Segmenting Medical MRI via Recurrent Decoding Cell
The encoder-decoder networks are commonly used in medical image segmentation
due to their remarkable performance in hierarchical feature fusion. However,
the expanding path for feature decoding and spatial recovery does not consider
the long-term dependency when fusing feature maps from different layers, and
the universal encoder-decoder network does not make full use of the
multi-modality information to improve the network robustness especially for
segmenting medical MRI. In this paper, we propose a novel feature fusion unit
called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to
memorize the long-term context information from the previous layers in the
decoding phase. An encoder-decoder network, named Convolutional Recurrent
Decoding Network (CRDN), is also proposed based on RDC for segmenting
multi-modality medical MRI. CRDN adopts CNN backbone to encode image features
and decode them hierarchically through a chain of RDCs to obtain the final
high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and
HVSMR datasets demonstrate that the introduction of RDC effectively improves
the segmentation accuracy as well as reduces the model size, and the proposed
CRDN owns its robustness to image noise and intensity non-uniformity in medical
MRI.Comment: 8 pages, 7 figures, AAAI-2
Deep Variational Luenberger-type Observer for Stochastic Video Prediction
Considering the inherent stochasticity and uncertainty, predicting future
video frames is exceptionally challenging. In this work, we study the problem
of video prediction by combining interpretability of stochastic state space
models and representation learning of deep neural networks. Our model builds
upon an variational encoder which transforms the input video into a latent
feature space and a Luenberger-type observer which captures the dynamic
evolution of the latent features. This enables the decomposition of videos into
static features and dynamics in an unsupervised manner. By deriving the
stability theory of the nonlinear Luenberger-type observer, the hidden states
in the feature space become insensitive with respect to the initial values,
which improves the robustness of the overall model. Furthermore, the
variational lower bound on the data log-likelihood can be derived to obtain the
tractable posterior prediction distribution based on the variational principle.
Finally, the experiments such as the Bouncing Balls dataset and the Pendulum
dataset are provided to demonstrate the proposed model outperforms concurrent
works