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Guest Editorial Generative Adversarial Networks in Biomedical Image Computing
2022
IEEE journal of biomedical and health informatics
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learning is used to extract domain-invariant features for output-level feature alignment. ...
[A8] propose an unsupervised domain adaptation segmentation framework for pancreatic cancer based on GCN and meta-learning strategy, which conducts encoders incorporating adversarial learning to separate ...
doi:10.1109/jbhi.2021.3134004
fatcat:dgokzbuwpvbrbjxd7ph7ihsgha
Front Matter: Volume 11313
2020
Medical Imaging 2020: Image Processing
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The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. ...
Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ...
of hyper-reflective foci in OCT images using a U-shape network 11313 09 Adversarial domain adaptation for multi-device retinal OCT segmentation
SESSION 2 IMAGE ANALYSIS IN ULTRASOUND AND OCT: JOINT ...
doi:10.1117/12.2570657
fatcat:be32besqknaybh6wibz7unuboa
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 1548-1561 Aligning Discriminative and Representative Features: An Unsupervised Domain Adaptation Method for Building Damage Assessment. ...
., +, TIP 2020 6386-6395
Aligning Discriminative and Representative Features: An Unsupervised
Domain Adaptation Method for Building Damage Assessment. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Domain adaptation model for retinopathy detection from cross-domain OCT images
2020
International Conference on Medical Imaging with Deep Learning
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As data labels are difficult to acquire, we proposed a generative adversarial network-based domain adaptation model to address the cross-domain OCT images classification task, which can extract invariant ...
Typically, OCT images captured from different devices show heterogeneous appearances because of different scan settings; thus, the DNN model trained from one domain may fail if applied directly to a new ...
In this work, we propose a domain adaptation model for OCT images, namely DAOCT. ...
dblp:conf/midl/WangCLKHJS20
fatcat:p66yqbditzc67itxyvdhedvioi
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan ...
Lu, C., +, JBHI Aug. 2020 2420-2429 Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification. ...
Guemes, A., +, JBHI May 2020 1439-1446 Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images
[article]
2019
arXiv
pre-print
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Adversarial learning with feature recalibration module (FRM) is applied to transfer informative knowledge from the domain feature spaces adaptively. ...
Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases. ...
[2] proposed a domain critic module (DCM) and a domain adaptation module (DAM) for the unsupervised crossmodality adaptation problem. ...
arXiv:1908.08242v2
fatcat:vcqoi3ouojb4ndygynnx5cwfnq
Unsupervised Domain Adaptation of Object Detectors: A Survey
[article]
2021
arXiv
pre-print
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Here, we describe in detail the domain adaptation problem for detection and present an extensive survey of the various methods. ...
There is a plethora of works to adapt classification and segmentation models to label-scarce target datasets through unsupervised domain adaptation. ...
[121] Adversarial feature learning
Image-to-image translation
Domain randomization Roychowdhury et al. [61] Khodabandeh et al. [62] Kim et al. [97] D'Innocente et al. ...
arXiv:2105.13502v2
fatcat:ozzbbvoflfdvjdewjnjmfajlpa
2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25
2021
IEEE journal of biomedical and health informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, JBHI April 2021 1292-1304 MCDCD: Multi-Source Unsupervised Domain Adaptation for Abnormal Human Gait Detection. ...
., +, JBHI April 2021 1070-1079 MCDCD: Multi-Source Unsupervised Domain Adaptation for Abnormal Human Gait Detection. ...
doi:10.1109/jbhi.2022.3140980
fatcat:ufig7b54gfftnj3mocspoqbzq4
Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging
2021
IEEE Transactions on Medical Imaging
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unpaired MR and CT scans, nucleus detection in cross-modality microscopy images, lung nodule detection in cross-protocol CT datasets, and various medical vision applications in cross-domain retinal imaging ...
Unsupervised domain adaption has emerged as an effective approach to improving the tolerance of deep learning models to the distribution shifts in medical imaging datasets. ...
doi:10.1109/tmi.2021.3089292
pmid:34795461
pmcid:PMC8594751
fatcat:t7kufjbdyfgazng3gcuyuhawxu
Table of contents
2020
IEEE Transactions on Image Processing
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Zheng 1061 Homologous Component Analysis for Domain Adaptation ............... Y. ...
Guan 3805 Graininess-Aware Deep Feature Learning for Robust Pedestrian Detection ..... C. Lin, J. Lu, G. Wang, and J. ...
doi:10.1109/tip.2019.2940372
fatcat:h23ul2rqazbstcho46uv3lunku
2021 Index IEEE Transactions on Industrial Informatics Vol. 17
2021
IEEE Transactions on Industrial Informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TII Oct. 2021 7114-7122 TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT. ...
., +, TII Oct. 2021 7114-7122 TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT. ...
doi:10.1109/tii.2021.3138206
fatcat:ulsazxgmpfdmlivigjqgyl7zre
Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
2022
Eye and Vision
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Results In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. ...
Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research. ...
Domain adaptation via the domain transfer function of a GAN may provide a chance to use a machine learning system in different settings. ...
doi:10.1186/s40662-022-00277-3
pmid:35109930
pmcid:PMC8808986
fatcat:z3oycm6ybrbs5bgrinxqba6g64
2018 Index IEEE Transactions on Biomedical Engineering Vol. 65
2018
IEEE Transactions on Biomedical Engineering
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1460-1467 Qureshi, M.N.I., Min, B., Park, H., Cho, D., Choi, W., and Lee, B., Multiclass Classification of Word Imagination Speech With Hybrid Connectivity Features; TBME Oct. 2168Oct ...
., Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing; TBME Nov. ...
Hossein Hosseini, S.A., +, TBME Oct. 2018 2365-2374 Electrophysiological Muscle Classification Using Multiple Instance Learning and Unsupervised Time and Spectral Domain Analysis. ...
doi:10.1109/tbme.2018.2890522
fatcat:xoblnegncrgmbmiu3r3hxntrxy
Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data
2023
Journal of Personalized Medicine
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These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. ...
This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. ...
Domains and tasks are different but related in unsupervised learning. Zhuang et al. ...
doi:10.3390/jpm13121703
pmid:38138930
pmcid:PMC10744730
fatcat:sv4cf6mumrc4xflve5ijnthwq4
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading
[article]
2022
arXiv
pre-print
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In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. ...
Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback ...
In other directions, authors in Tsai et al. (2018) have argued that adaptation to the structural output space is favorable for unsupervised domain adaptation in semantic segmentation tasks. ...
arXiv:2212.14615v1
fatcat:rk4xeluk3zeffaz57fbc57kddq
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