Bettina Baeßler
@Baessler_Rad
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UKW #radiologist; #radiomics; #MachineLearning; #deeplearning; #AI; #CMR; education; #diversity; #inclusion; @DRG.de; @EurRadiology
Würzburg, Germany
Joined November 2017
Fresh off the press @EurRadiology Advancing radiomics research translation through a public database https://t.co/pIWpUZf85u Commentary to an article by @Tugba_Akinci_MD @Baessler_Rad @renatocuocolo @pintodrad
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Congratulations to the winners! 👏🥳 Well deserved. #EuropeanRadiology @renatocuocolo @cannella_rob
https://t.co/DyDTApD2bQ
@EurRadiology @InsightsImaging @EuSoMII
link.springer.com
European Radiology - The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically...
@EuSoMII Award to the most cited papers in @InsightsImaging and @EurRadiology! Congratulations to the winners 🏆 🏆 @renatocuocolo @pintodrad
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Interesting study on the reproducibility of radiomics 👇 #EuropeanRadiology
Using a convolutional neural network-based image conversion technique significantly improves the reproducibility of radiomic features in hepatocellular carcinomas. (Heejin Lee et al) #EuropeanRadiology 🔗 https://t.co/0UYMI2iYJT
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🤩 Hurray! The 17th chapter on AI in Radiology from @myESR 's Undergraduate Education e-book is out! 👏Thanks all the @EuSoMII contributors! Available fo free https://t.co/RJjHUWD3NY
#AI #MedicalImaging #Education #eBook
#FreeResource #RadiologyAI
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This study demonstrates the validity and reliability of automated ASPECTS evaluation for supporting neurologists in the clinical care process for acute ischemic stroke patients. (Shu Wan et al) #EuropeanRadiology 🔗 https://t.co/Wd2JOaQvNH
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The reproducibility across different VOI sizes in normal #liver #MRI was improved when translating images into parametric maps before feature extraction. (Laura Jacqueline Jensen et al.) #EuropeanRadiologyExperimental 🔗 https://t.co/Ng7kNCJGLv
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I really like the new graphical abstracts in @EurRadiology! 👏 What do you think of it?
📣 Introducing this exciting new meta-research by Dr. Hameed et al (@MairaHameed_) from @UCLHresearch @DoM_UCL published in #EuropeanRadiology Follow the 🧵 for more insight by Dr. Hameed 1/6
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Application of #AI-derived contours yields results comparable to manual segmentations. (Jan Gröschel et al.) #EuropeanRadiology Read more here 👉 https://t.co/ut3xW957dv
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Interesting study, thanks for sharing @BernhardKainz1 !
📢 Just out! A leap in object segmentation using pre-trained latent diffusion models! Generate accurate foreground-background models from textual descriptions WITHOUT segmentation labels. 🚀 Surpasses prior methods and nears fully supervised training. 🩺#AIResearch @BorderlessSci
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Critical Review: #Radiology #ArtificialIntelligence, a systematic evaluation of methods (RAISE). (@BSKellyRad et al.) #InsightsIntoImaging Want to learn more? Click here ➡️ https://t.co/PF4frpvL7d
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Educational Review: Role of diagnostic imaging in psoriatic #arthritis - how, when, and why. (Ana María Crespo-Rodríguez et al.) #InsightsIntoImaging 🔗 https://t.co/NfZplZsQPN
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Still an underrecognized problem, but a tremendously important one (of course, not only in #radiology)! Interesting read 👇👇 #EuropeanRadiology
How prevalent is #burnout among #radiology residents and what are the risks? (Ziqi Wan et al.) #EuropeanRadiology Want to read more? Click the link below ⬇️ https://t.co/FLMqDffcGN
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Commentary: Generalizability of prostate #MRI deep learning: does one size fit all data? (@StanzioneMD & @renatocuocolo) #EuropeanRadiology Commentary 👉 https://t.co/4kDTy4f5dn Original Article 👉 https://t.co/ubv86vcHyU
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Commentary: Explainable #AI - current status and future potential. (@BasvanderVelden) #EuropeanRadiology Read the full commentary here ➡️ https://t.co/5ybFiZhvlL
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Apps for radiology residency are due soon. Do you know how competitive it's been? We reviewed match data: https://t.co/cweHIvqkYb with bonus thoughts in podcast: https://t.co/dqvpRlQL7p Google https://t.co/8xWe2FP9c9; Apple https://t.co/rYml558hoD; Spotify https://t.co/Kn9nGFO0UP
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Thanks for sharing @Tugba_Akinci_MD! Looking forward to seeing more studies adhering to checklists like #CLAIM in the future. @EurRadiology
Using the @Radiology_AI #CLAIM checklist is a crucial component in ensuring high-quality reporting of AI research in radiology According to the recently published CLAIM citation analysis: 🧵👇
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The reproducibility of RQS is extremely low. This has now been highlighted in @EurRadiology and should be taken into account in future studies assessing the quality of radiomics analyses. @Tugba_Akinci_MD @renatocuocolo @EuSoMII
The RQS has undoubtedly had an important role in raising awareness on #radiomics and #MachineLearning research. It's also showing limitations, as seen in the latest @EuSoMII Radiomics Auditing Group paper. Now available on @EurRadiology (#openaccess).
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Congrats on this important work, dear Radiomics Auditing Group! @EuSoMII
The RQS has undoubtedly had an important role in raising awareness on #radiomics and #MachineLearning research. It's also showing limitations, as seen in the latest @EuSoMII Radiomics Auditing Group paper. Now available on @EurRadiology (#openaccess).
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#Cardiac implantable electronic device-related artefacts may reduce the diagnostic value of cardiac magnetic resonance. (Aino-Maija Vuorinen et al.) #EuropeanRadiology #RadiologyHeadToToe Read more here 👉 https://t.co/Xo8W5FmuCA
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Thanks @_akhaliq for featuring this work! A full thread on the methods/implications is https://t.co/hmyQ1gSI8b
Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts paper page: https://t.co/X3epYmQgL0 Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large
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