{"title":"Achieving More with Less: Combining Strong and Weak Labels for Intracranial Hemorrhage Detection.","authors":"Tugba Akinci D'Antonoli, Jeffrey D Rudie","doi":"10.1148/ryai.240670","DOIUrl":"10.1148/ryai.240670","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"6 6","pages":"e240670"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gianluca Brugnara, Chandrakanth Jayachandran Preetha, Katerina Deike, Robert Haase, Thomas Pinetz, Martha Foltyn-Dumitru, Mustafa A Mahmutoglu, Brigitte Wildemann, Ricarda Diem, Wolfgang Wick, Alexander Radbruch, Martin Bendszus, Hagen Meredig, Aditya Rastogi, Philipp Vollmuth
Lisong Dai, Jiayu Lei, Fenglong Ma, Zheng Sun, Haiyan Du, Houwang Zhang, Jingxuan Jiang, Jianyong Wei, Dan Wang, Guang Tan, Xinyu Song, Jinyu Zhu, Qianqian Zhao, Songtao Ai, Ai Shang, Zhaohui Li, Ya Zhang, Yuehua Li
Purpose To guide the attention of a deep learning (DL) model toward MRI characteristics of brain lesions by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective study, 35 282 brain MRI scans (January 2018 to June 2023) and corresponding radiology reports from center 1 were used for training, validation, and internal testing. A total of 2655 brain MRI scans (January 2022 to December 2022) from centers 2-5 were reserved for external testing. Textual features were extracted from radiology reports to guide a DL model (ReportGuidedNet) focusing on lesion characteristics. Another DL model (PlainNet) without textual features was developed for comparative analysis. Both models identified 15 conditions, including 14 diseases and normal brains. Performance of each model was assessed by calculating macro-averaged area under the receiver operating characteristic curve (ma-AUC) and micro-averaged AUC (mi-AUC). Attention maps, which visualized model attention, were assessed with a five-point Likert scale. Results ReportGuidedNet outperformed PlainNet for all diagnoses on both internal (ma-AUC, 0.93 [95% CI: 0.91, 0.95] vs 0.85 [95% CI: 0.81, 0.88]; mi-AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.89 [95% CI: 0.83, 0.92]) and external (ma-AUC, 0.91 [95% CI: 0.88, 0.93] vs 0.75 [95% CI: 0.72, 0.79]; mi-AUC, 0.90 [95% CI: 0.87, 0.92] vs 0.76 [95% CI: 0.72, 0.80]) testing sets. The performance difference between internal and external testing sets was smaller for ReportGuidedNet than for PlainNet (Δma-AUC, 0.03 vs 0.10; Δmi-AUC, 0.02 vs 0.13). The Likert scale score of ReportGuidedNet was higher than that of PlainNet (mean ± SD: 2.50 ± 1.09 vs 1.32 ± 1.20; P < .001). Conclusion The integration of radiology report textual features improved the ability of the DL model to detect brain lesions, thereby enhancing interpretability and generalizability. Keywords: Deep Learning, Computer-aided Diagnosis, Knowledge-driven Model, Radiology Report, Brain MRI Supplemental material is available for this article. Published under a CC BY 4.0 license.
{"title":"Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.","authors":"Lisong Dai, Jiayu Lei, Fenglong Ma, Zheng Sun, Haiyan Du, Houwang Zhang, Jingxuan Jiang, Jianyong Wei, Dan Wang, Guang Tan, Xinyu Song, Jinyu Zhu, Qianqian Zhao, Songtao Ai, Ai Shang, Zhaohui Li, Ya Zhang, Yuehua Li","doi":"10.1148/ryai.230520","DOIUrl":"10.1148/ryai.230520","url":null,"abstract":"<p><p>Purpose To guide the attention of a deep learning (DL) model toward MRI characteristics of brain lesions by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective study, 35 282 brain MRI scans (January 2018 to June 2023) and corresponding radiology reports from center 1 were used for training, validation, and internal testing. A total of 2655 brain MRI scans (January 2022 to December 2022) from centers 2-5 were reserved for external testing. Textual features were extracted from radiology reports to guide a DL model (ReportGuidedNet) focusing on lesion characteristics. Another DL model (PlainNet) without textual features was developed for comparative analysis. Both models identified 15 conditions, including 14 diseases and normal brains. Performance of each model was assessed by calculating macro-averaged area under the receiver operating characteristic curve (ma-AUC) and micro-averaged AUC (mi-AUC). Attention maps, which visualized model attention, were assessed with a five-point Likert scale. Results ReportGuidedNet outperformed PlainNet for all diagnoses on both internal (ma-AUC, 0.93 [95% CI: 0.91, 0.95] vs 0.85 [95% CI: 0.81, 0.88]; mi-AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.89 [95% CI: 0.83, 0.92]) and external (ma-AUC, 0.91 [95% CI: 0.88, 0.93] vs 0.75 [95% CI: 0.72, 0.79]; mi-AUC, 0.90 [95% CI: 0.87, 0.92] vs 0.76 [95% CI: 0.72, 0.80]) testing sets. The performance difference between internal and external testing sets was smaller for ReportGuidedNet than for PlainNet (Δma-AUC, 0.03 vs 0.10; Δmi-AUC, 0.02 vs 0.13). The Likert scale score of ReportGuidedNet was higher than that of PlainNet (mean ± SD: 2.50 ± 1.09 vs 1.32 ± 1.20; <i>P</i> < .001). Conclusion The integration of radiology report textual features improved the ability of the DL model to detect brain lesions, thereby enhancing interpretability and generalizability. <b>Keywords:</b> Deep Learning, Computer-aided Diagnosis, Knowledge-driven Model, Radiology Report, Brain MRI <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230520"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142393849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yunan Wu, Michael Iorga, Suvarna Badhe, James Zhang, Donald R Cantrell, Elaine J Tanhehco, Nicholas Szrama, Andrew M Naidech, Michael Drakopoulos, Shamis T Hasan, Kunal M Patel, Tarek A Hijaz, Eric J Russell, Shamal Lalvani, Amit Adate, Todd B Parrish, Aggelos K Katsaggelos, Virginia B Hill
Krzysztof Bartnik, Tomasz Bartczak, Mateusz Krzyziński, Krzysztof Korzeniowski, Krzysztof Lamparski, Piotr Węgrzyn, Eric Lam, Mateusz Bartkowiak, Tadeusz Wróblewski, Katarzyna Mech, Magdalena Januszewicz, Przemysław Biecek
{"title":"WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data.","authors":"Krzysztof Bartnik, Tomasz Bartczak, Mateusz Krzyziński, Krzysztof Korzeniowski, Krzysztof Lamparski, Piotr Węgrzyn, Eric Lam, Mateusz Bartkowiak, Tadeusz Wróblewski, Katarzyna Mech, Magdalena Januszewicz, Przemysław Biecek","doi":"10.1148/ryai.240296","DOIUrl":"10.1148/ryai.240296","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240296"},"PeriodicalIF":8.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeffrey D Rudie, Hui-Ming Lin, Robyn L Ball, Sabeena Jalal, Luciano M Prevedello, Savvas Nicolaou, Brett S Marinelli, Adam E Flanders, Kirti Magudia, George Shih, Melissa A Davis, John Mongan, Peter D Chang, Ferco H Berger, Sebastiaan Hermans, Meng Law, Tyler Richards, Jan-Peter Grunz, Andreas Steven Kunz, Shobhit Mathur, Sandro Galea-Soler, Andrew D Chung, Saif Afat, Chin-Chi Kuo, Layal Aweidah, Ana Villanueva Campos, Arjuna Somasundaram, Felipe Antonio Sanchez Tijmes, Attaporn Jantarangkoon, Leonardo Kayat Bittencourt, Michael Brassil, Ayoub El Hajjami, Hakan Dogan, Muris Becircic, Agrahara G Bharatkumar, Eduardo Moreno Júdice de Mattos Farina, Errol Colak