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Weakly supervised segmentation from extreme points
[article]
2019
arXiv
pre-print
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We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. ...
Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. ...
Discussion & Conclusions We presented a method for weakly supervised 3D segmentation from extreme points. ...
arXiv:1910.01236v1
fatcat:joub4vndynfllbhkqma6fsdhim
Inter Extreme Points Geodesics for Weakly Supervised Segmentation
[article]
2021
arXiv
pre-print
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From the extreme points, 3D bounding boxes are extracted around objects of interest. ...
We introduce InExtremIS, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects ...
In this work, we propose a novel weakly supervised approach to learn automatic image segmentation using extreme points as weak annotations during training, here a set of manual extreme points along each ...
arXiv:2107.00583v1
fatcat:ncozlnfitzdgbmd5r6t7qy635y
Going to Extremes: Weakly Supervised Medical Image Segmentation
2021
Machine Learning and Knowledge Extraction
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An initial segmentation is generated based on the extreme points using the random walker algorithm. ...
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. ...
Effect of Extreme Point Noise during Inference
Discussion We provided a method for weakly supervised 3D segmentation from extreme points. ...
doi:10.3390/make3020026
fatcat:vpy3rtl63rctjcv3sgtd7mn56u
Going to Extremes: Weakly Supervised Medical Image Segmentation
[article]
2020
arXiv
pre-print
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An initial segmentation is generated based on the extreme points utilizing the random walker algorithm. ...
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. ...
Discussion We provided a method for weakly-supervised 3D segmentation from extreme points. ...
arXiv:2009.11988v1
fatcat:msm35eefarharcaldcmjhr3wzm
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
[article]
2018
arXiv
pre-print
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To alleviate the burden of manual annotation, in this paper, we propose a new weakly supervised DL approach for biomedical image segmentation using boxes only annotation. ...
performance over the best known state-of-the-art weakly supervised DL method and is able to achieve (1) nearly the same accuracy compared to fully supervised DL methods with far less annotation effort ...
A set of weakly supervised annotation methods has been proposed for semantic segmentation in natural scene images. ...
arXiv:1806.00593v1
fatcat:hxyrku6qcfc4vljkjwikjp34du
Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations
[article]
2022
arXiv
pre-print
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Recently, weakly-supervised image segmentation using weak annotations like scribbles has gained great attention, since such annotations are much easier to obtain compared to time-consuming and label-intensive ...
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary prediction. ...
(i.e., UNet PCE [17] and MAAG [18] ) and a weakly-supervised method using extreme points [8] . ...
arXiv:2205.06779v1
fatcat:o7y6ha62zbbiri3yfiy4mscfay
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.02246v3 fatcat:2mydhdvxunhvfbdtr3d452ynnuOther Versions
Experimental results on both ScanNet-v2 and S3DIS show that our self-training approach, with extremely-sparse annotations, outperforms all existing weakly supervised methods for 3D semantic segmentation ...
Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare. ...
Weakly Supervised 3D Semantic Segmentation Compared with fully supervised 3D semantic segmentation, weakly supervised 3D semantic segmentation is relatively under-explored. ...
arXiv:2104.02246v4
fatcat:ucniyqpo6zgi5k4ned7my5nafq
Weakly Supervised Multiclass Video Segmentation
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation. ...
Whereas previous weakly supervised video segmentation methods have been limited to the two-class case, our proposed scheme focuses on more challenging multiclass video segmentation, which finds a semantically ...
Weakly Supervised Multiclass Video Segmentation Weakly labeled videos are first segmented into spatiotemporal supervoxels, which are represented as high dimensional points in the feature space, and weakly ...
doi:10.1109/cvpr.2014.15
dblp:conf/cvpr/LiuTSRCB14
fatcat:wdbnf4p2vjh7bpy23j25ahhb5y
One-shot Weakly-Supervised Segmentation in Medical Images
[article]
2021
arXiv
pre-print
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One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and utilizing coarse labels instead ...
Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings. ...
Weakly Supervised Segmentation Compared with fully supervised segmentation task which needs time-consuming pixel-wise annotations, WSS requires more flexible annotations like scribbles [32] , bounding ...
arXiv:2111.10773v1
fatcat:bzryc4hkqnabbifopr3icimdku
PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision
[article]
2020
arXiv
pre-print
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This paper presents a novel procedure for producing semantic segmentation from images given some point level annotations. ...
Several methods have shown that we can learn semantic segmentation from less expensive image-level labels, but the effectiveness of point level labels, a healthy compromise between all pixels labelled ...
Conclusion While numerous methods for using image-level supervision for weakly supervised semantic segmentation have been introduced recently, using point supervision has been largely unexplored. ...
arXiv:2007.05615v1
fatcat:qa3locldtjcajgicvnt4ttnfii
GECNN for Weakly Supervised Semantic Segmentation of 3D Point Clouds
2021
IEICE transactions on information and systems
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This paper presents a novel method for weakly supervised semantic segmentation of 3D point clouds using a novel graph and edge convolutional neural network (GECNN) towards 1% and 10% point cloud with labels ...
It inherently handles the challenges of segmenting sparse 3D point clouds with limited annotations in a large scale point cloud space. ...
[4] propose to employ conditional random field for weakly supervised segmentation of urban scenes from 3D LiDAR point clouds. ...
doi:10.1587/transinf.2021edp7134
fatcat:iy6ctacj4rddvgfbltcuvybwb4
Medical image segmentation with imperfect 3D bounding boxes
[article]
2021
arXiv
pre-print
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When the tightness is improved by our solution, the results of the weakly-supervised segmentation become much closer to those of the fully-supervised one. ...
The effectiveness of our solution is demonstrated by evaluating a known weakly-supervised segmentation approach with and without the proposed bounding box correction algorithm. ...
It is straightforward to produce a 3D bounding box of the object by finding its extreme points in the three coordinate axes. ...
arXiv:2108.03300v1
fatcat:an4te3vgnncbdkf3xul336zqom
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
[article]
2022
arXiv
pre-print
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Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning. ...
Experiments on weakly supervised 3D point cloud segmentation tasks validate the efficacy of proposed method in particular at low-label regime. ...
Architecture Overview To formally define the weakly-supervised 3D point segmentation task, we follow the settings proposed in [1] . ...
arXiv:2205.03137v1
fatcat:bdu7ytk5pjaqnb62dzhtznnlom
WAILS: Watershed Algorithm with Image-level Supervision for Weakly Supervised Semantic Segmentation
2019
IEEE Access
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INDEX TERMS Semantic segmentation, weakly supervised, watershed algorithm. ...
First, the image is coarsely segmented through a weakly supervised network at the image level. ...
Some classic weakly supervised image semantic segmentation label methods include point, scribble, and bounding box. For example, Bearman et al. [10] and Kwak et al. ...
doi:10.1109/access.2019.2908216
fatcat:tj55fscpqbfmvbpiftdrhkrkme
Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization
[article]
2021
arXiv
pre-print
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This work enables the training of semantic segmentation networks on images with only a single point for training per instance, an extreme case of weak supervision which drastically reduces the burden of ...
We achieve competitive results against the state-of-the-art in point-supervised semantic segmentation on challenging datasets in digital pathology. ...
which enables weakly supervised training. ...
arXiv:2103.05617v2
fatcat:h3phricp6jfqrcynsqxf2e4vui
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