74 research outputs found
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Medical image processing tasks such as segmentation often require capturing
non-local information. As organs, bones, and tissues share common
characteristics such as intensity, shape, and texture, the contextual
information plays a critical role in correctly labeling them. Segmentation and
labeling is now typically done with convolutional neural networks (CNNs) but
the context of the CNN is limited by the receptive field which itself is
limited by memory requirements and other properties. In this paper, we propose
a new attention module, that we call Permutohedral Attention Module (PAM), to
efficiently capture non-local characteristics of the image. The proposed method
is both memory and computationally efficient. We provide a GPU implementation
of this module suitable for 3D medical imaging problems. We demonstrate the
efficiency and scalability of our module with the challenging task of vertebrae
segmentation and labeling where context plays a crucial role because of the
very similar appearance of different vertebrae.Comment: Accepted at MICCAI-201
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
In this work, we tackle the problem of instance segmentation, the task of
simultaneously solving object detection and semantic segmentation. Towards this
goal, we present a model, called MaskLab, which produces three outputs: box
detection, semantic segmentation, and direction prediction. Building on top of
the Faster-RCNN object detector, the predicted boxes provide accurate
localization of object instances. Within each region of interest, MaskLab
performs foreground/background segmentation by combining semantic and direction
prediction. Semantic segmentation assists the model in distinguishing between
objects of different semantic classes including background, while the direction
prediction, estimating each pixel's direction towards its corresponding center,
allows separating instances of the same semantic class. Moreover, we explore
the effect of incorporating recent successful methods from both segmentation
and detection (i.e. atrous convolution and hypercolumn). Our proposed model is
evaluated on the COCO instance segmentation benchmark and shows comparable
performance with other state-of-art models.Comment: 10 pages including referenc
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