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Going to Extremes: Weakly Supervised Medical Image Segmentation
2021
Machine Learning and Knowledge Extraction
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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. ...
This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. ...
Previous work primarily used boundary box annotations for weakly supervised learning in 2D/3D medical imaging, such as Rajchl et al. [29] . ...
doi:10.3390/make3020026
fatcat:vpy3rtl63rctjcv3sgtd7mn56u
Going to Extremes: Weakly Supervised Medical Image Segmentation
[article]
2020
arXiv
pre-print
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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. ...
This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. ...
Discussion We provided a method for weakly-supervised 3D segmentation from extreme points. ...
arXiv:2009.11988v1
fatcat:msm35eefarharcaldcmjhr3wzm
One-shot Weakly-Supervised Segmentation in Medical Images
[article]
2021
arXiv
pre-print
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Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings. ...
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. ...
Conclusion and Discussion In this work, we present a novel one-shot medical image segmentation framework, which incorporates one-shot localization and weakly-supervised segmentation. ...
arXiv:2111.10773v1
fatcat:bzryc4hkqnabbifopr3icimdku
Semi-supervised and weakly-supervised learning with spatio-temporal priors in medical image segmentation
2021
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In the thesis, we also open new avenues for future research using AI with limited annotations, which we believe is key to developing robust AI models for medical image analysis. ...
In our work, we pay special attention to the problem of automating semantic segmentation, where an image is partitioned into multiple semantically meaningful regions, separating the anatomical components ...
“Going to Extremes: Weakly Supervised Medical Image
Segmentation”. In: arXiv preprint arXiv:2009.11988. ...
doi:10.13118/imtlucca/e-theses/344/
fatcat:qru63k6hibed3pwtxemhd523ua
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
[chapter]
2017
Lecture Notes in Computer Science
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2018 pre-print arXiv:1807.10819v1 fatcat:he4ndk2akba3nkg6bxoeapq5le
Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent ...
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. ...
Furthermore, weakly-supervised training, with only a point and radius provided for each training nodule, yields results competitive with those of full segmentation. ...
doi:10.1007/978-3-319-66179-7_73
fatcat:5z5igwron5g3dcwov76lpa3coa
ACC/AHA 2005 Practice Guidelines for the Management of Patients With Peripheral Arterial Disease (Lower Extremity, Renal, Mesenteric, and Abdominal Aortic)
2006
Circulation
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The ABI correlates only weakly with treadmill-based walking ability for any individual patient. ...
Normally, the pulsatility index increases from the more proximal to the more distal segments of the lower extremities (186) . ...
doi:10.1161/circulationaha.106.174526
pmid:16549646
fatcat:a6bznmry5bas7dvzvvztcxp7zq
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2111.02403v2 fatcat:yateaiwunbdtflaps3uwxf7l7uOther Versions
Recently, deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training. ...
Although many efforts in this task, there are still few large image datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. ...
We also would like to thank the Shanghai AI Lab and Shanghai SenseTime Research for their high-performance computation support. ...
arXiv:2111.02403v3
fatcat:c3qcnobmfbhhlj5nq5bgmwkyli
Software challenges in extreme scale systems
2009
Journal of Physics, Conference Series
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them, and scaling multiple chips to complete systems, for a range of real system applications, from highly scalable deep space exploration to trans-petaflops level supercomputing. ...
He also leads the UPC language effort, a consortium of industry and academic research institutions aiming to produce a unified approach to parallel C programming based on global address space methods. ...
Going into the Extreme Scale computing arena, the importance of formal methods is bound to escalate significantly. ...
doi:10.1088/1742-6596/180/1/012045
fatcat:iukutry2dvbitfdh6ng7kgz564
Constrained Deep Weak Supervision for Histopathology Image Segmentation
2017
IEEE Transactions on Medical Imaging
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2017 pre-print arXiv:1701.00794v1 fatcat:ci3ec7xlizbwlaufmjqrarwxgy
In this paper, we develop a new weakly-supervised learning algorithm to learn to segment cancerous regions in histopathology images. ...
The contributions of our algorithm are threefold: (1) We build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCN) in which image-to-image weakly-supervised ...
ACKNOWLEDGMENT We would like to thank Lab of Pathology and Pathophysiology, Zhejiang University in China for providing data and help. ...
doi:10.1109/tmi.2017.2724070
pmid:28692971
fatcat:h27cegcib5cabpbtaiboqir5e4
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel framework to segment the target tissues accurately for the AS-OCT images, by using the combination of weakly-annotated images (majority) and fully-annotated images (minority ...
Therefore, the proposed method is demonstrated to be effective in exploiting information contained in the weakly-annotated images and has the capability to substantively relieve the annotation workload ...
Conclusion In this work, we proposed a macro-micro weakly-supervised framework to tackle the problem of cornea and iris segmentation for the AS-OCT images. ...
arXiv:2007.10007v1
fatcat:fgwyzuk2xbfp5f7cphv2vq4jii
AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
[article]
2021
arXiv
pre-print
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To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and ...
on distinct medical centers, phases, and unseen diseases. ...
methods: semi-supervised learning, weakly supervised learning, and continual learning, which are increasingly drawing attention in the medical image analysis community. ...
arXiv:2010.14808v2
fatcat:hsfrknwdlffovdtqyuoi5cp24a
Towards Single Stage Weakly Supervised Semantic Segmentation
[article]
2021
arXiv
pre-print
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The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or box labels. ...
Current state-of-the-art (SOTA) models leverage image-level labels to produce class activation maps (CAMs) which go through multiple stages of refinement before they are thresholded to make pseudo-masks ...
Deep extreme cut: From extreme points age segmentation. In Proceedings of the IEEE Conference
to object segmentation. ...
arXiv:2106.10309v2
fatcat:l3oafc7rz5frbiynru2vn6ogfa
Weakly Supervised Object Localization and Detection: A Survey
[article]
2021
arXiv
pre-print
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supervised object localization and detection methods, and potential future directions to further promote the development of this research field. ...
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems ...
These priors can be used to guide the weakly supervised learning process on medical imaging data. ...
arXiv:2104.07918v1
fatcat:dwl6sjfzibdilnvjnrbifp4uke
Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans
[article]
2020
arXiv
pre-print
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We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. ...
The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. ...
CT scan analysis in medical image segmentation In the medical image segmentation field, one of the most important problems is the segmentation of anatomical structures for medical diagnosis [7] . ...
arXiv:2004.08270v1
fatcat:6uqdkpyofzcyvd3phdbo4g6vxu
Cascaded Interactional Targeting Network for Egocentric Video Analysis
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve stateof-the-art hand segmentation performance. ...
Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations ...
This work was also partially supported to Dr. Qi Tian by ARO grants W911NF-15-1-0290 and Faculty Research Gift Awards by NEC Laboratories of America and Blippar. ...
doi:10.1109/cvpr.2016.210
dblp:conf/cvpr/ZhouNHYT16
fatcat:krp3kzvyrzaq5fvjaxspzkp2fm
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