8,404 research outputs found
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
We present Im2Pano3D, a convolutional neural network that generates a dense
prediction of 3D structure and a probability distribution of semantic labels
for a full 360 panoramic view of an indoor scene when given only a partial
observation (<= 50%) in the form of an RGB-D image. To make this possible,
Im2Pano3D leverages strong contextual priors learned from large-scale synthetic
and real-world indoor scenes. To ease the prediction of 3D structure, we
propose to parameterize 3D surfaces with their plane equations and train the
model to predict these parameters directly. To provide meaningful training
supervision, we use multiple loss functions that consider both pixel level
accuracy and global context consistency. Experiments demon- strate that
Im2Pano3D is able to predict the semantics and 3D structure of the unobserved
scene with more than 56% pixel accuracy and less than 0.52m average distance
error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain
tumor segmentation. However, it's common to have missing imaging modalities in
clinical practice. Since there exists a strong correlation between multi
modalities, a novel correlation representation block is proposed to specially
discover the latent multi-source correlation. Thanks to the obtained
correlation representation, the segmentation becomes more robust in the case of
missing modalities. The model parameter estimation module first maps the
individual representation produced by each encoder to obtain independent
parameters, then, under these parameters, the correlation expression module
transforms all the individual representations to form a latent multi-source
correlation representation. Finally, the correlation representations across
modalities are fused via the attention mechanism into a shared representation
to emphasize the most important features for segmentation. We evaluate our
model on BraTS 2018 datasets, it outperforms the current state-of-the-art
method and produces robust results when one or more modalities are missing.Comment: 9 pages, 6 figures, accepted by MICCAI 202
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence
(AAAI) 201
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
In medical vision, different imaging modalities provide complementary
information. However, in practice, not all modalities may be available during
inference or even training. Previous approaches, e.g., knowledge distillation
or image synthesis, often assume the availability of full modalities for all
patients during training; this is unrealistic and impractical due to the
variability in data collection across sites. We propose a novel approach to
learn enhanced modality-agnostic representations by employing a meta-learning
strategy in training, even when only limited full modality samples are
available. Meta-learning enhances partial modality representations to full
modality representations by meta-training on partial modality data and
meta-testing on limited full modality samples. Additionally, we co-supervise
this feature enrichment by introducing an auxiliary adversarial learning
branch. More specifically, a missing modality detector is used as a
discriminator to mimic the full modality setting. Our segmentation framework
significantly outperforms state-of-the-art brain tumor segmentation techniques
in missing modality scenarios.Comment: Accepted in ICCV 202
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