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Distributed Contrastive Learning for Medical Image Segmentation [article]

Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu
2022 arXiv   pre-print
In this work, we propose two federated self-supervised learning frameworks for volumetric medical image segmentation with limited annotations.  ...  However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.  ...  CONCLUSION This work aims to enable federated contrastive learning (FCL) for volumetric medical image segmentation with limited annotations.  ... 
arXiv:2208.03808v1 fatcat:3ruylvqxkfbzbo7db5oiafsupa

Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation [article]

Ran Gu, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Xiaofan Zhang, Guotai Wang, Shaoting Zhang
2022 arXiv   pre-print
To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.  ...  Efficiently utilizing discriminative features is crucial for convolutional neural networks to achieve remarkable performance in medical image segmentation and is also important for model generalization  ...  In this work, we propose a Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.  ... 
arXiv:2205.06551v1 fatcat:yghftldfondrrnqdkurcvgs5ba

Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation [article]

Jinxi Xiang, Zhuowei Li, Wenji Wang, Qing Xia, Shaoting Zhang
2021 arXiv   pre-print
In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique.  ...  Deep learning has demonstrated significant improvements in medical image segmentation using a sufficiently large amount of training data with manual labels.  ...  However, the applicability of contrastive learning in medical image segmentation is yet to be explored [6] .  ... 
arXiv:2105.12924v2 fatcat:sadhwoifbbbn7otbe4j6urtqji

Contrastive Registration for Unsupervised Medical Image Segmentation [article]

Lihao Liu, Angelica I Aviles-Rivero, Carola-Bibiane Schönlieb
2022 arXiv   pre-print
In this work, we present a novel optimisation model framed into a new CNN-based contrastive registration architecture for unsupervised medical image segmentation.  ...  Firstly, we propose an architecture to capture the image-to-image transformation pattern via registration for unsupervised medical image segmentation.  ...  Trust project Unveiling the invisible, the European Union Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 777826 NoMADS, the Cantab Capital Institute for  ... 
arXiv:2011.08894v3 fatcat:2lmsahntrrf4jjlifioplcjx2a

Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation [article]

Jizong Peng, Ping Wang, Marco Pedersoli, Christian Desrosiers
2022 arXiv   pre-print
Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.  ...  However, it is not trivial to build reasonable pairs for a segmentation task in an unsupervised way.  ...  Among these self-supervised methods, contrastive learning has become a prevailing strategy for pre-training medical image segmentation models (Chaitanya et al., 2020; Zeng et al., 2021; Peng et al., 2021b  ... 
arXiv:2202.02371v2 fatcat:sup2l4iahjdl3dajxcytmmmwsq

Towards to Robust and Generalized Medical Image Segmentation Framework [article]

Yurong Chen
2022 arXiv   pre-print
Therefore, we propose a novel two-stage framework for robust generalized medical image segmentation.  ...  Deep learning-based computer-aided diagnosis is gradually deployed to review and analyze medical images.  ...  Secondly, these pretext tasks, such as predicting the rotation of the image, and contrastive learning strategies are inessential for the medical image.  ... 
arXiv:2108.03823v7 fatcat:kuov3ikdxbefvlppveq2zsx4dy

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation [article]

Rushi Jiao, Yichi Zhang, Le Ding, Rong Cai, Jicong Zhang
2022 arXiv   pre-print
In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results.  ...  Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches.  ...  for medical image segmentation [141] .  ... 
arXiv:2207.14191v2 fatcat:j3o3vg5vd5dsxg5i3y6ovxlrey

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation [article]

Chenyu You, Weicheng Dai, Lawrence Staib, James S. Duncan
2022 arXiv   pre-print
Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation.  ...  Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images.  ...  In this work, we present a principled framework called Anatomical-aware ConTrastive dIstillatiON (ACTION), for multi-class medical image segmentation.  ... 
arXiv:2206.02307v1 fatcat:6uygotfvz5anljiz5gwmaz54pq

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation [article]

Devavrat Tomar, Behzad Bozorgtabar, Manana Lortkipanidze, Guillaume Vray, Mohammad Saeed Rad, Jean-Philippe Thiran
2021 arXiv   pre-print
Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations  ...  In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data.  ...  Conclusion and Future Work We proposed the novel volumetric contrastive loss used for style transfer by leveraging unlabeled data for oneshot medical image segmentation.  ... 
arXiv:2110.02117v1 fatcat:ampolwvdi5hkrcz4goo5lcjlh4

Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation [article]

Yuxin Kang, Hansheng Li, Xuan Zhao, Dongqing Hu, Feihong Liu, Lei Cui, Jun Feng, Lin Yang
2022 arXiv   pre-print
While achieving remarkable success for medical image segmentation, deep convolution neural networks (DCNNs) often fail to maintain their robustness when confronting test data with the novel distribution  ...  Extensive experimental results on two typical medical image segmentation tasks show that our approach performs better than state-of-the-art domain generalization methods.  ...  For medical image segmentation, several latest methods explored various data augmentation techniques [13, 14] and meta-learning paradigm [10, 12] to guarantee no distribution gap between training and  ... 
arXiv:2205.02845v1 fatcat:l4djjwctnnhvld2zyyzzwa4xdm

MR Image Segmentation Based on Contrast Enhancement with Collaborative Learning

Yuchou Chang
2018 Advances In Image and Video Processing  
A collaborative learning based image enhancement is firstly applied on low contrast MR brain image. Then, spectral clustering algorithm is used for segmenting enhanced image.  ...  Image contrast needs to be enhanced for better post-processing and image analysis.  ...  Due to the complexity and diversity of medical images, no segmentation algorithm is suitable for all images.  ... 
doi:10.14738/aivp.61.4089 fatcat:5qu2rgxz2zfj7l54jy5uwx7ciu

Adversarial normalization for multi domain image segmentation [article]

Pierre-Luc Delisle, Benoit Anctil-Robitaille, Christian Desrosiers, Herve Lombaert
2020 arXiv   pre-print
To solve this problem, we propose an adversarial normalization approach for image segmentation which learns common normalizing functions across multiple datasets while retaining image realism.  ...  Image normalization is a critical step in medical imaging.  ...  dataset, 2) a learned normalization network for medical images which produces images that are realistic and interpretable by clinicians.  ... 
arXiv:1912.00993v2 fatcat:glonvyyycnalnbgagb4yp5apaa

Federated Contrastive Learning for Volumetric Medical Image Segmentation [chapter]

Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu
2021 Lecture Notes in Computer Science  
In this work, we propose an FCL framework for volumetric medical image segmentation with limited annotations.  ...  However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.  ...  learning for volumetric medical image segmentation with limited annotations.  ... 
doi:10.1007/978-3-030-87199-4_35 fatcat:h5ekgozd45b4vab43djcsxvr6q

Personalizing Federated Medical Image Segmentation via Local Calibration [article]

Jiacheng Wang, Yueming Jin, Liansheng Wang
2022 arXiv   pre-print
Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets.  ...  Effectiveness of our method has been verified on three medical image segmentation tasks with different modalities, where our method consistently shows superior performance to the state-of-the-art personalized  ...  Collaborative training using the data across multiple medical sites is increasingly essential for yielding the maximal potential of deep models for medical image segmentation [38, 9, 28] .  ... 
arXiv:2207.04655v1 fatcat:p424253nqbaerljhjcoff6rnzm

ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation [article]

Samayan Bhattacharya, Sk Shahnawaz, Avigyan Bhattacharya
2022 arXiv   pre-print
In this paper we use contrastive learning to train Unet++ for semantic segmentation of medical images using medical images from various sources including magnetic resonance imaging (MRI) and computed tomography  ...  Unets have become the standard method for semantic segmentation of medical images, along with fully convolutional networks (FCN).  ...  Contrastive learning [73] is commonly used for self-supervised learning.  ... 
arXiv:2206.05225v1 fatcat:7a7k2wfjv5eazm5zvq4yoqbb7m
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