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Going to Extremes: Weakly Supervised Medical Image Segmentation

Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
2021 Machine Learning and Knowledge Extraction  
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]

Holger R Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
2020 arXiv   pre-print
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]

Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang
2021 arXiv   pre-print
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

Gabriele Valvano
2021
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]

Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
2017 Lecture Notes in Computer Science  
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  
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]

Xiangde Luo, Wenjun Liao, Jianghong Xiao, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang
2022 arXiv   pre-print
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

Vivek Sarkar, William Harrod, Allan E Snavely
2009 Journal of Physics, Conference Series  
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

Zhipeng Jia, Xingyi Huang, Eric I-Chao Chang, Yan Xu
2017 IEEE Transactions on Medical Imaging  
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]

Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng
2020 arXiv   pre-print
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]

Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang (+5 others)
2021 arXiv   pre-print
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]

Peri Akiva, Kristin Dana
2021 arXiv   pre-print
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]

Dingwen Zhang, Junwei Han, Gong Cheng, Ming-Hsuan Yang
2021 arXiv   pre-print
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]

Avik Hati, Matteo Bustreo, Diego Sona, Vittorio Murino, Alessio Del Bue
2020 arXiv   pre-print
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

Yang Zhou, Bingbing Ni, Richang Hong, Xiaokang Yang, Qi Tian
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
) 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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