48 research outputs found
A General Framework for Saliency Detection Methods
Saliency detection is one of the most challenging problems in the fields of
image analysis and computer vision. Many approaches propose different
architectures based on the psychological and biological properties of the human
visual attention system. However, there is not still an abstract framework,
which summarized the existed methods. In this paper, we offered a general
framework for saliency models, which consists of five main steps:
pre-processing, feature extraction, saliency map generation, saliency map
combination, and post-processing. Also, we study different saliency models
containing each level and compare their performance together. This framework
helps researchers to have a comprehensive view of studying new methods.Comment: 5 pages 3 figure
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Colonoscopy is an effective technique for detecting colorectal polyps, which
are highly related to colorectal cancer. In clinical practice, segmenting
polyps from colonoscopy images is of great importance since it provides
valuable information for diagnosis and surgery. However, accurate polyp
segmentation is a challenging task, for two major reasons: (i) the same type of
polyps has a diversity of size, color and texture; and (ii) the boundary
between a polyp and its surrounding mucosa is not sharp. To address these
challenges, we propose a parallel reverse attention network (PraNet) for
accurate polyp segmentation in colonoscopy images. Specifically, we first
aggregate the features in high-level layers using a parallel partial decoder
(PPD). Based on the combined feature, we then generate a global map as the
initial guidance area for the following components. In addition, we mine the
boundary cues using a reverse attention (RA) module, which is able to establish
the relationship between areas and boundary cues. Thanks to the recurrent
cooperation mechanism between areas and boundaries, our PraNet is capable of
calibrating any misaligned predictions, improving the segmentation accuracy.
Quantitative and qualitative evaluations on five challenging datasets across
six metrics show that our PraNet improves the segmentation accuracy
significantly, and presents a number of advantages in terms of
generalizability, and real-time segmentation efficiency.Comment: Accepted to MICCAI 202
Learning To Pay Attention To Mistakes
In convolutional neural network based medical image segmentation, the
periphery of foreground regions representing malignant tissues may be
disproportionately assigned as belonging to the background class of healthy
tissues
\cite{attenUnet}\cite{AttenUnet2018}\cite{InterSeg}\cite{UnetFrontNeuro}\cite{LearnActiveContour}.
This leads to high false negative detection rates. In this paper, we propose a
novel attention mechanism to directly address such high false negative rates,
called Paying Attention to Mistakes. Our attention mechanism steers the models
towards false positive identification, which counters the existing bias towards
false negatives. The proposed mechanism has two complementary implementations:
(a) "explicit" steering of the model to attend to a larger Effective Receptive
Field on the foreground areas; (b) "implicit" steering towards false positives,
by attending to a smaller Effective Receptive Field on the background areas. We
validated our methods on three tasks: 1) binary dense prediction between
vehicles and the background using CityScapes; 2) Enhanced Tumour Core
segmentation with multi-modal MRI scans in BRATS2018; 3) segmenting stroke
lesions using ultrasound images in ISLES2018. We compared our methods with
state-of-the-art attention mechanisms in medical imaging, including
self-attention, spatial-attention and spatial-channel mixed attention. Across
all of the three different tasks, our models consistently outperform the
baseline models in Intersection over Union (IoU) and/or Hausdorff Distance
(HD). For instance, in the second task, the "explicit" implementation of our
mechanism reduces the HD of the best baseline by more than , whilst
improving the IoU by more than . We believe our proposed attention
mechanism can benefit a wide range of medical and computer vision tasks, which
suffer from over-detection of background.Comment: Accepted at BMVC 202
Global Context-Aware Progressive Aggregation Network for Salient Object Detection
Deep convolutional neural networks have achieved competitive performance in
salient object detection, in which how to learn effective and comprehensive
features plays a critical role. Most of the previous works mainly adopted
multiple level feature integration yet ignored the gap between different
features. Besides, there also exists a dilution process of high-level features
as they passed on the top-down pathway. To remedy these issues, we propose a
novel network named GCPANet to effectively integrate low-level appearance
features, high-level semantic features, and global context features through
some progressive context-aware Feature Interweaved Aggregation (FIA) modules
and generate the saliency map in a supervised way. Moreover, a Head Attention
(HA) module is used to reduce information redundancy and enhance the top layers
features by leveraging the spatial and channel-wise attention, and the Self
Refinement (SR) module is utilized to further refine and heighten the input
features. Furthermore, we design the Global Context Flow (GCF) module to
generate the global context information at different stages, which aims to
learn the relationship among different salient regions and alleviate the
dilution effect of high-level features. Experimental results on six benchmark
datasets demonstrate that the proposed approach outperforms the
state-of-the-art methods both quantitatively and qualitatively