1,992 research outputs found
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
The main obstacle to weakly supervised semantic image segmentation is the
difficulty of obtaining pixel-level information from coarse image-level
annotations. Most methods based on image-level annotations use localization
maps obtained from the classifier, but these only focus on the small
discriminative parts of objects and do not capture precise boundaries.
FickleNet explores diverse combinations of locations on feature maps created by
generic deep neural networks. It selects hidden units randomly and then uses
them to obtain activation scores for image classification. FickleNet implicitly
learns the coherence of each location in the feature maps, resulting in a
localization map which identifies both discriminative and other parts of
objects. The ensemble effects are obtained from a single network by selecting
random hidden unit pairs, which means that a variety of localization maps are
generated from a single image. Our approach does not require any additional
training steps and only adds a simple layer to a standard convolutional neural
network; nevertheless it outperforms recent comparable techniques on the Pascal
VOC 2012 benchmark in both weakly and semi-supervised settings.Comment: To appear in CVPR 201
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
Designing deep learning algorithms for gland segmentation is crucial for
automatic cancer diagnosis and prognosis, yet the expensive annotation cost
hinders the development and application of this technology. In this paper, we
make a first attempt to explore a deep learning method for unsupervised gland
segmentation, where no manual annotations are required. Existing unsupervised
semantic segmentation methods encounter a huge challenge on gland images: They
either over-segment a gland into many fractions or under-segment the gland
regions by confusing many of them with the background. To overcome this
challenge, our key insight is to introduce an empirical cue about gland
morphology as extra knowledge to guide the segmentation process. To this end,
we propose a novel Morphology-inspired method via Selective Semantic Grouping.
We first leverage the empirical cue to selectively mine out proposals for gland
sub-regions with variant appearances. Then, a Morphology-aware Semantic
Grouping module is employed to summarize the overall information about the
gland by explicitly grouping the semantics of its sub-region proposals. In this
way, the final segmentation network could learn comprehensive knowledge about
glands and produce well-delineated, complete predictions. We conduct
experiments on GlaS dataset and CRAG dataset. Our method exceeds the
second-best counterpart over 10.56% at mIOU.Comment: MICCAI 2023 Accepte
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