11,081 Hits in 5.5 sec

Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images

Yi Zhou, Xiaodong He, Lei Huang, Li Liu, Fan Zhu, Shanshan Cui, Ling Shao
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we propose a collaborative learning method to jointly improve the performance of disease grading and lesion segmentation by semi-supervised learning with an attention mechanism.  ...  Meanwhile, the lesion attention model can refine the lesion maps using class-specific information to fine-tune the segmentation model in a semi-supervised manner.  ...  Conclusion In this paper, we proposed a collaborative learning method of semi-supervised lesion segmentation and disease grading for medical imaging.  ... 
doi:10.1109/cvpr.2019.00218 dblp:conf/cvpr/ZhouHH00C019 fatcat:lj5s56anlraghm2g4tds6cuta4

Self-Supervised, Semi-Supervised, Multi-Context Learning for the Combined Classification and Segmentation of Medical Images (Student Abstract)

Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Yuan Xiao, Dingjun Hao, Zhen Qian, Demetri Terzopoulos
crucial medical imaging tasks, classification and segmentation.  ...  We propose a novel semi-supervised multiple-task model leveraging self-supervision and adversarial training—namely, self-supervised, semi-supervised, multi-context learning (S4MCL)—and apply it to two  ...  We propose a self-supervised, semi-supervised, multi-context learning (S 4 MCL) model that combines the advantages of selfsupervised learning, adversarial learning, and multi-task learning for use in real-world  ... 
doi:10.1609/aaai.v34i10.7179 fatcat:to4vkda2jjge7mfhaihofccz7i

Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net

Justin L. Wang, Hassan Farooq, Hanqi Zhuang, Ali K. Ibrahim
2020 Applied Sciences  
In this paper, we propose a modified u-net and curriculum learning strategy using a multi-task semi-supervised attention-based model, initially introduced by Chen et al., to segment ICH sub-groups from  ...  However, due to a limited number of labeled medical images available, which often causes poor model accuracy in terms of the Dice coefficient, there is much to be improved.  ...  [5] created a curriculum-style strategy for a semi-supervised CNN designed for segmentation tasks based on inequality constraints.  ... 
doi:10.3390/app10093297 fatcat:l7yujvcvqfcxngik2vd6hhjfo4

Semi-Supervised Multi-Task Learning With Chest X-Ray Images [article]

Abdullah-Al-Zubaer Imran, Demetri Terzopoulos
2019 arXiv   pre-print
We propose a novel multi-task learning model for jointly learning a classifier and a segmentor, from chest X-ray images, through semi-supervised learning.  ...  Based on our experimental results using a novel segmentation model, an Adversarial Pyramid Progressive Attention U-Net (APPAU-Net), we hypothesize that KLTV can be more effective for generalizing multi-tasking  ...  Our technical contributions are twofold: (1) a novel multi-task learning model for semi-supervised classification and segmentation from small labeled medical image datasets and (2) a new loss function  ... 
arXiv:1908.03693v2 fatcat:hfsucymq4bcpvdrn33c4bgbnve

Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation [article]

Yichi Zhang, Qingcheng Liao, Rushi Jiao, Jicong Zhang
2021 arXiv   pre-print
Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data  ...  Medical image segmentation is a fundamental and critical step in many clinical approaches.  ...  Zhang, “Dual-task mutual learning for semi-supervised [6] to further improve and validate the performance of semi- medical image segmentation,” in Pattern Recognition and Computer supervised  ... 
arXiv:2112.02508v1 fatcat:ofgv42dygvhyxphgh2wbcgdvoy

Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey [article]

S Niyas, S J Pawan, M Anand Kumar, Jeny Rajan
2022 arXiv   pre-print
Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation.  ...  At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis.  ...  segmentation models (c) Multi-task learning models 2. 3D CNN with Semi-supervised learning 3. 3D CNN with Weakly-supervised learning 4.  ... 
arXiv:2108.08467v3 fatcat:s2rzghycjbczpparmrflsdzujq

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
: scaling up to large unlabelled medical datasets 342 AtlasNet: Multi-atlas non-linear deep networks for medical image segmentation 345 Multiple Instance Learning for Heterogeneous Images: Training a CNN  ...  246 Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays 248 Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Label-Efficient Multi-Task Segmentation using Contrastive Learning [article]

Junichiro Iwasawa, Yuichiro Hirano, Yohei Sugawara
2020 arXiv   pre-print
Obtaining annotations for 3D medical images is expensive and time-consuming, despite its importance for automating segmentation tasks.  ...  In this study, we propose a multi-task segmentation model with a contrastive learning based subtask and compare its performance with other multi-task models, varying the number of labeled data for training  ...  Acknowledgments J.I. was supported by the Grant-in-Aid for JSPS Fellows JP18J21942.  ... 
arXiv:2009.11160v1 fatcat:end42iytbjcslmlweu3bxua2ce

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  application of deep learning models in medical image segmentation.  ...  For semi-segmentation of multi-organ from 3D medical images, Zhou et al.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
application of deep learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  For semi-segmentation of multi-organ from 3D medical images, Zhou et al.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Recent advances and clinical applications of deep learning in medical image analysis [article]

Xuxin Chen, Ximin Wang, Ke Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
2021 arXiv   pre-print
Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application  ...  Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging  ...  Semi-and self-supervised learning based segmenting models For medical image segmentation, to alleviate the need for a large amount of annotated training data, reserachers have adopted generative models  ... 
arXiv:2105.13381v2 fatcat:2k342a6rhjaavpoa2qoqxhg5rq

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  ...  This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and  ...  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

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao
2020 IEEE Transactions on Medical Imaging  
Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and  ...  Our semi-supervised framework can improve the learning ability and achieve a higher performance.  ...  In addition, semi-supervised learning has been widely applied in medical segmentation task, where a frequent issue is the lack of pixel-level labeled data, even when large scale set of unlabeled image  ... 
doi:10.1109/tmi.2020.2996645 pmid:32730213 fatcat:227q3yiporecdjxeixcj4jemhe

A Survey of Cross-Modality Brain Image Synthesis [article]

Guoyang Xie, Jinbao Wang, Yawen Huang, Yefeng Zheng, Feng Zheng, Yaochu Jin
2022 arXiv   pre-print
In this paper, we tend to approach multi-modality brain image synthesis task from different perspectives, which include the level of supervision, the range of modality synthesis, and the synthesis-based  ...  A realistic solution is to explore either an unsupervised learning or a semi-supervised learning to synthesize the absent neuroimaging data.  ...  [Yu et al.] introduce semi- Synthesis from Applies For brain lesion With downstream With downstream supervised MRI image to unsupervised detection classification task segmentation task learning PET image  ... 
arXiv:2202.06997v2 fatcat:kqxte2xrcrcpjfkkhwrcxdjqsu

Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images [article]

Shuailin Li, Chuyu Zhang, Xuming He
2020 arXiv   pre-print
Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep  ...  To achieve this, we develop a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces.  ...  Based on this SDM representation, we then design a semi-supervised learning loss for training the segmentation network.  ... 
arXiv:2007.10732v1 fatcat:cfb2ifmgo5barov2onzxhy2aoy
« Previous Showing results 1 — 15 out of 11,081 results