Simplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online)最新文献
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-16902-1
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We present a method for classifying tasks in fetal ultrasound scans using the eye-tracking data of sonographers. The visual attention of a sonographer captured by eye-tracking data over time is defined by a scanpath. In routine fetal ultrasound, the captured standard imaging planes are visually inconsistent due to fetal position, movements, and sonographer scanning experience. To address this challenge, we propose a scale and position invariant task classification method using normalised visual scanpaths. We describe a normalisation method that uses bounding boxes to provide the gaze with a reference to the position and scale of the imaging plane and use the normalised scanpath sequences to train machine learning models for discriminating between ultrasound tasks. We compare the proposed method to existing work considering raw eyetracking data. The best performing model achieves the F1-score of 84% and outperforms existing models.
{"title":"Towards Scale and Position Invariant Task Classification using Normalised Visual Scanpaths in Clinical Fetal Ultrasound.","authors":"Clare Teng, Harshita Sharma, Lior Drukker, Aris T Papageorghiou, J Alison Noble","doi":"10.1007/978-3-030-87583-1_13","DOIUrl":"10.1007/978-3-030-87583-1_13","url":null,"abstract":"<p><p>We present a method for classifying tasks in fetal ultrasound scans using the eye-tracking data of sonographers. The visual attention of a sonographer captured by eye-tracking data over time is defined by a scanpath. In routine fetal ultrasound, the captured standard imaging planes are visually inconsistent due to fetal position, movements, and sonographer scanning experience. To address this challenge, we propose a scale and position invariant task classification method using normalised visual scanpaths. We describe a normalisation method that uses bounding boxes to provide the gaze with a reference to the position and scale of the imaging plane and use the normalised scanpath sequences to train machine learning models for discriminating between ultrasound tasks. We compare the proposed method to existing work considering raw eyetracking data. The best performing model achieves the F1-score of 84% and outperforms existing models.</p>","PeriodicalId":93620,"journal":{"name":"Simplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online)","volume":"10 1","pages":"129-138"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83220001","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}
Pub Date : 2021-09-21DOI: 10.1007/978-3-030-87583-1_2
Arijit Patra, Yifan Cai, Pierre Chatelain, Harshita Sharma, Lior Drukker, Aris T Papageorghiou, J Alison Noble
Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a different and difficult challenge due to the tendency of reduction in performance over old classes while adapting to new ones. Controlling such a 'forgetting' is vital for deployed algorithms to evolve with new arrivals of data incrementally. Usually, incremental learning approaches rely on expert knowledge in the form of manual annotations or active feedback. In this paper, we explore the role that other forms of expert knowledge might play in making deep networks in medical image analysis immune to forgetting over extended time. We introduce a novel framework for mitigation of this forgetting effect in deep networks considering the case of combining ultrasound video with point-of-gaze tracked for expert sonographers during model training. This is used along with a novel weighted distillation strategy to reduce the propagation of effects due to class imbalance.
{"title":"Multimodal Continual Learning with Sonographer Eye-Tracking in Fetal Ultrasound.","authors":"Arijit Patra, Yifan Cai, Pierre Chatelain, Harshita Sharma, Lior Drukker, Aris T Papageorghiou, J Alison Noble","doi":"10.1007/978-3-030-87583-1_2","DOIUrl":"10.1007/978-3-030-87583-1_2","url":null,"abstract":"<p><p>Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a different and difficult challenge due to the tendency of reduction in performance over old classes while adapting to new ones. Controlling such a 'forgetting' is vital for deployed algorithms to evolve with new arrivals of data incrementally. Usually, incremental learning approaches rely on expert knowledge in the form of manual annotations or active feedback. In this paper, we explore the role that other forms of expert knowledge might play in making deep networks in medical image analysis immune to forgetting over extended time. We introduce a novel framework for mitigation of this forgetting effect in deep networks considering the case of combining ultrasound video with point-of-gaze tracked for expert sonographers during model training. This is used along with a novel weighted distillation strategy to reduce the propagation of effects due to class imbalance.</p>","PeriodicalId":93620,"journal":{"name":"Simplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online)","volume":"44 1","pages":"14-24"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86853980","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}
Pub Date : 2021-01-01DOI: 10.1007/978-3-030-87583-1
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Simplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online)
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