56,727 research outputs found
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and robustness. In the meanwhile, existing approaches usually deal
with these two tasks independently, which would neglect the close relatedness
of them. In this paper, we present a novel method of sibling fully
convolutional network with prior objectness interaction (called SFCN-OPI) to
tackle the two tasks simultaneously and interactively using a unified
end-to-end framework. Specifically, the sibling FCN branches share features in
earlier layers while holding respective higher layers for specific tasks. More
importantly, the detection branch outputs the objectness prior which
dynamically interacts with the fine-grained classification sibling branch
during the training and testing processes. With this mechanism, the
fine-grained classification successfully focuses on regions with high
confidence of nuclei existence and outputs the conditional probability, which
in turn benefits the detection through back propagation. Extensive experiments
on colon cancer histology images have validated the effectiveness of our
proposed SFCN-OPI and our method has outperformed the state-of-the-art methods
by a large margin.Comment: Accepted at AAAI 201
Integrated Deep and Shallow Networks for Salient Object Detection
Deep convolutional neural network (CNN) based salient object detection
methods have achieved state-of-the-art performance and outperform those
unsupervised methods with a wide margin. In this paper, we propose to integrate
deep and unsupervised saliency for salient object detection under a unified
framework. Specifically, our method takes results of unsupervised saliency
(Robust Background Detection, RBD) and normalized color images as inputs, and
directly learns an end-to-end mapping between inputs and the corresponding
saliency maps. The color images are fed into a Fully Convolutional Neural
Networks (FCNN) adapted from semantic segmentation to exploit high-level
semantic cues for salient object detection. Then the results from deep FCNN and
RBD are concatenated to feed into a shallow network to map the concatenated
feature maps to saliency maps. Finally, to obtain a spatially consistent
saliency map with sharp object boundaries, we fuse superpixel level saliency
map at multi-scale. Extensive experimental results on 8 benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches with a margin.Comment: Accepted by IEEE International Conference on Image Processing (ICIP)
201
Single-Shot Refinement Neural Network for Object Detection
For object detection, the two-stage approach (e.g., Faster R-CNN) has been
achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has
the advantage of high efficiency. To inherit the merits of both while
overcoming their disadvantages, in this paper, we propose a novel single-shot
based detector, called RefineDet, that achieves better accuracy than two-stage
methods and maintains comparable efficiency of one-stage methods. RefineDet
consists of two inter-connected modules, namely, the anchor refinement module
and the object detection module. Specifically, the former aims to (1) filter
out negative anchors to reduce search space for the classifier, and (2)
coarsely adjust the locations and sizes of anchors to provide better
initialization for the subsequent regressor. The latter module takes the
refined anchors as the input from the former to further improve the regression
and predict multi-class label. Meanwhile, we design a transfer connection block
to transfer the features in the anchor refinement module to predict locations,
sizes and class labels of objects in the object detection module. The
multi-task loss function enables us to train the whole network in an end-to-end
way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO
demonstrate that RefineDet achieves state-of-the-art detection accuracy with
high efficiency. Code is available at https://github.com/sfzhang15/RefineDetComment: 14 pages, 7 figures, 7 table
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