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[2307.07829] HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
[2307.07829] HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
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Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2307.07829 (eess)
[Submitted on 15 Jul 2023]
Title:HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Authors:Chunming He, Kai Li, Guoxia Xu, Jiangpeng Yan, Longxiang Tang, Yulun Zhang, Xiu Li, Yaowei Wang Download a PDF of the paper titled HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance, by Chunming He and 6 other authors
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Abstract:Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this paper, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multi-encoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets, including two newly collected datasets, verify that the proposed method outperforms existing techniques in terms of both enhancement quality and downstream task performance. We will make the code and the newly collected datasets publicly available for community study.
Comments:
14 pages, 10 figures
Subjects:
Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2307.07829 [eess.IV]
(or
arXiv:2307.07829v1 [eess.IV] for this version)
https://doi.org/10.48550/arXiv.2307.07829
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arXiv-issued DOI via DataCite
Submission history From: Chunming He [view email] [v1]
Sat, 15 Jul 2023 15:26:25 UTC (8,438 KB)
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mainBranchesTagsGo to fileCodeFolders and filesNameNameLast commit messageLast commit dateLatest commit History11 Commitsconfigsconfigs datasetsdatasets modelsmodels transformstransforms utilsutils Comparison.pngComparison.png Framework.pngFramework.png README.mdREADME.md demo.pydemo.py evaluate_idx.pyevaluate_idx.py mask_gen.pymask_gen.py pre_train.pypre_train.py requirements.txtrequirements.txt rnw_star.yamlrnw_star.yaml train.pytrain.py View all filesRepository files navigationREADMEHQG-Net_TNNLS
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance, TNNLS
[Paper] [Datasets] [Models]
Authors
Chunming He, Kai Li*, Guoxia Xu, Longxiang Tang, Jiangpeng Yan, Yulun Zhang, Xiu Li*, Yaowei Wang
Abstract: Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this paper, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multi-encoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets, including two newly collected datasets, verify that the proposed method outperforms existing techniques in terms of both enhancement quality and downstream task performance. We will make the code and the newly collected datasets publicly available for community study.
Environment
You can install all the requirements via:
pip install -r requirements.txt
Train
python train.py
Test
python demo.py
Related Work
Structure and illumination constrained GAN for medical image enhancement, TMI21.
Citation
Concat
If you have any questions, please feel free to contact me via email at chunminghe19990224@gmail.com or hcm21@mails.tsinghua.edu.cn.
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While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this paper, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multi-encoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets, including two newly collected datasets, verify that the proposed method outperforms existing techniques in terms of both enhancement quality and downstream task performance. 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aba Group Holding Limited acquired HQG, Inc. from NetEase, Inc. for CNY 13 billion. -September 04, 2019 | MarketScreenerad31dafdd13430c0.T_zW5hxGTcI57zWZaixF-zMYdzrtRU9gzgGtXmPyULA.NpKFgFgrHZF8qGyoGUAyy2pdH2CmNiMviEj8JhW_Btw1lZ-VaTEY8WymfA
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ALIBABA GROUP HOLDING LIMITED Add to a listAdd to a listTo use this feature you must be a memberLog inSign upBackPDF Report
Alibaba Group Holding Limited
Equities
BABA
US01609W1027
Internet Services
Market Closed -
Nyse
Other stock markets
04:00:42 2024-03-06 pm EST
After market
07:49:16 pm
73.71
USD
+2.28%
74.05
+0.46%
Mar. 06
JD.com Reports Higher Quarterly Profit and Revenue Amid Weak Economy
DJ
Mar. 06
HK stocks rebound, China flat as investors await signals from parliament meeting
RE
Summary
Quotes
Charts
News
Ratings
Calendar
Company
Financials
Consensus
Revisions
Funds and ETFs
Alibaba Group Holding Limited acquired HQG, Inc. from NetEase, Inc. for CNY 13 billion.
September 04, 2019
Share
Alibaba Group Holding Limited (NYSE:BABA) agreed to acquire HQG, Inc. from NetEase, Inc. (NasdaqGS:NTES) for CNY 13 billion on September 5, 2019. The consideration is comprised of CNY 10.01 billion in cash payable to NetEase, which includes the repayment of certain loans made by NetEase to HQG and to HQG equity award holders, as well as approximately 14.3 million Alibaba ordinary shares, equivalent to approximately 1.8 million American depositary shares issued to NetEase and a contingent cash consideration not exceeding CNY 700 million payable upon the satisfaction of certain non-compete provisions by the selling equity holders. The purchase price is subject to adjustments and certain payment conditions under the terms of the definitive agreements. In a related transaction, Alibaba and NetEase have entered into a definitive agreement for Alibaba, together with Yunfeng, to invest approximately $700 million in NetEase Cloud Music in its latest round of financing. Alibaba plans for HQG, Inc. to continue to operate independently under its current brand. Tmall Import and Export General Manager Alvin Liu will serve as HQG, Inc.'s new Chief Executive Officer. The transaction is subject to regulatory approval.
Tim Gardner and Chris Welty of Weil, Gotshal & Manges LLP, Hong Kong acted as legal advisors for Alibaba Group Holding Limited. Paul W. Boltz of Gibson, Dunn & Crutcher LLP acted as legal advisor to NetEase, Inc. Jonathan Zhou of Fangda Partners acted as the legal advisor to Alibaba Group Holding Limited.
Alibaba Group Holding Limited (NYSE:BABA) completed the acquisition of HQG, Inc. from NetEase, Inc. (NasdaqGS:NTES) on September 5, 2019. Share
© S&P Capital IQ - 2019
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Chart Alibaba Group Holding Limited DurationAuto.2 months3 months6 months9 months1 year2 years5 years10 yearsMax.PeriodDayWeek
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Company Profile
Alibaba Group Holding Limited is the leading online Chinese marketplace. The group provides computing platform allowing individuals and professionals to make their buying and selling transactions of goods and services. The activity is organized around 3 areas:
- operation of e-commerce platform: Websites holding (Alibaba.com, Taobao.com, Tmall.com, Juhuasuan.com, Aliexpress.com, 1688.com, etc.);
- online payment services: services ensured through the Alipay.com platform;
- other: development of price comparison, interface and Web application portals, dematerialized management platforms of computing infrastructure, etc.
Sector
Internet Services
Calendar
2024-05-23
- Q4 2024 Earnings Release (Projected)
More about the company
Income Statement Evolution
More financial data
Analysis / Opinion
Alibaba Group Holding Limited : Investors are disappointed
November 21, 2023 at 10:25 am EST
IPO to watch: Alibaba's logistics arm files for Hong Kong listing
September 27, 2023 at 05:01 am EDT
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C-More Ratings
Analysts' Consensus
SellBuyMean consensusBUYNumber of Analysts45Last Close Price
530.5
CNYAverage target price
780.3
CNYSpread / Average Target+47.08%ConsensusEPS Revisions
Estimates Revisions
Quarterly earnings - Rate of surprise
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Sector
E-commerce & Auction Services
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Capi.
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