{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:28:55Z","timestamp":1775579335293,"version":"3.50.1"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82102027"],"award-info":[{"award-number":["82102027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Explorer Program of Shanghai Municipality","award":["23TS1400300"],"award-info":[{"award-number":["23TS1400300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tpami.2024.3399098","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T17:30:40Z","timestamp":1715707840000},"page":"7983-7997","source":"Crossref","is-referenced-by-count":20,"title":["Measurement Guidance in Diffusion Models: Insight from Medical Image Synthesis"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8032-371X","authenticated-orcid":false,"given":"Yimin","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Radiology, Weill Medical College, Cornell University, New York, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8380-6881","authenticated-orcid":false,"given":"Qinyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0611-0589","authenticated-orcid":false,"given":"Yuheng","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4709-5185","authenticated-orcid":false,"given":"Haikun","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9664-4967","authenticated-orcid":false,"given":"Menghan","family":"Xia","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2814538"},{"key":"ref2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref3","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Song"},{"key":"ref4","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020"},{"key":"ref5","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dhariwal"},{"key":"ref6","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref8","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Saharia"},{"key":"ref9","article-title":"Text-guided synthesis of artistic images with retrieval-augmented diffusion models","author":"Rombach","year":"2022"},{"key":"ref10","first-page":"12438","article-title":"Improved techniques for training score-based generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Song"},{"key":"ref11","first-page":"1415","article-title":"Maximum likelihood training of score-based diffusion models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Song"},{"key":"ref12","first-page":"12533","article-title":"D2C: Diffusion-decoding models for few-shot conditional generation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sinha"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19790-1_26"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530104"},{"key":"ref15","first-page":"23593","article-title":"Denoising diffusion restoration models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kawar"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01209"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01767"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"ref20","article-title":"SDEdit: Guided image synthesis and editing with stochastic differential equations","author":"Meng","year":"2021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530757"},{"key":"ref22","article-title":"EGSDE: Unpaired image-to-image translation via energy-guided stochastic differential equations","author":"Zhao","year":"2022"},{"key":"ref23","article-title":"Pretraining is all you need for image-to-image translation","author":"Wang","year":"2022"},{"key":"ref24","article-title":"VQBB: Image-to-image translation with vector quantized brownian bridge","author":"Li","year":"2022"},{"key":"ref25","article-title":"The swiss army knife for image-to-image translation: Multi-task diffusion models","author":"Wolleb","year":"2022"},{"key":"ref26","article-title":"Diffusion probabilistic models beat GANs on medical images","author":"Franzes","year":"2022"},{"key":"ref27","article-title":"Hierarchical text-conditional image generation with clip latents","author":"Ramesh","year":"2022"},{"key":"ref28","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Heusel"},{"key":"ref29","article-title":"Improved techniques for training GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Salimans"},{"key":"ref30","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref32","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dickstein"},{"key":"ref33","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020"},{"key":"ref34","first-page":"11287","article-title":"Score-based generative modeling in latent space","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vahdat"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI53787.2023.10230346"},{"key":"ref36","article-title":"Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images","author":"Akrout","year":"2023"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acca5c"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_48"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6619"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging9030069"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.10.015"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3172976"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16431-6_51"},{"key":"ref45","article-title":"Three-dimensional medical image synthesis with denoising diffusion probabilistic models","author":"Dorjsembe","year":"2022","journal-title":"Medical Imaging with Deep Learning"},{"key":"ref46","first-page":"7498","article-title":"Simple and principled uncertainty estimation with deterministic deep learning via distance awareness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref47","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lakshminarayanan"},{"key":"ref48","article-title":"Pitfalls of in-domain uncertainty estimation and ensembling in deep learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ashukha","year":"2019"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref50","article-title":"Variational dropout and the local reparameterization trick","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Kingma"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3006437"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102532"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3123461"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101790"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100003"},{"key":"ref56","article-title":"Classifier-free diffusion guidance","author":"Ho","year":"2022"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01118"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4149(82)90051-5"},{"key":"ref59","first-page":"22117","article-title":"Unsupervised representation learning from pre-trained diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref60","article-title":"Classification accuracy score for conditional generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ravuri"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.5566\/ias.1155"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863"},{"key":"ref65","first-page":"17","article-title":"Kaggle diabetic retinopathy detection competition report","volume":"22","author":"Graham","year":"2015","journal-title":"Univ. Warwick"},{"key":"ref66","article-title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5 mb model size","author":"Iandola","year":"2016"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10746266\/10530514.pdf?arnumber=10530514","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:17:15Z","timestamp":1732666635000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10530514\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":68,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3399098","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}