Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online)最新文献

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part VII 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10日至15日，会议录，第七部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68787-8

引用次数: 3

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part I 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10-15日，会议录，第一部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68763-2

引用次数: 0

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part IV 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10日至15日，会议录，第四部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68799-1

引用次数: 0

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part II 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10日至15日，会议录，第二部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68790-8

引用次数: 0

Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits. 用生命体征评估镰状细胞病患者住院期间的疼痛强度

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 Epub Date: 2021-02-23 DOI: 10.1007/978-3-030-68790-8_7

Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Arvind Subramaniam, Daniel M Abrams, Gary K Nave, Nirmish Shah

{"title":"Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits.","authors":"Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Arvind Subramaniam, Daniel M Abrams, Gary K Nave, Nirmish Shah","doi":"10.1007/978-3-030-68790-8_7","DOIUrl":"https://doi.org/10.1007/978-3-030-68790-8_7","url":null,"abstract":"Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0-10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0-5, severe pain: 6-10) at an inter-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.","PeriodicalId":93349,"journal":{"name":"Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online)","volume":"12662 ","pages":"77-85"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319918/pdf/nihms-1715101.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39266878","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}

引用次数: 3

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part III 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10日至15日，会议录，第三部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68796-0

引用次数: 2

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part VI 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10日至15日，会议录，第六部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68780-9

引用次数: 0

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part VIII 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10-15日，会议录，第八部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68793-9

引用次数: 2

Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part V 模式识别。ICPR国际研讨会和挑战:虚拟事件，2021年1月10日至15日，会议录，第五部分

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-68821-9

引用次数: 1

Self-Attentive Adversarial Stain Normalization. 自关注对抗性染色归一化。

Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online) Pub Date : 2021-01-01 Epub Date: 2021-02-21 DOI: 10.1007/978-3-030-68763-2_10

Aman Shrivastava, William Adorno, Yash Sharma, Lubaina Ehsan, S Asad Ali, Sean R Moore, Beatrice Amadi, Paul Kelly, Sana Syed, Donald E Brown

{"title":"Self-Attentive Adversarial Stain Normalization.","authors":"Aman Shrivastava, William Adorno, Yash Sharma, Lubaina Ehsan, S Asad Ali, Sean R Moore, Beatrice Amadi, Paul Kelly, Sana Syed, Donald E Brown","doi":"10.1007/978-3-030-68763-2_10","DOIUrl":"https://doi.org/10.1007/978-3-030-68763-2_10","url":null,"abstract":"Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. Traditionally proposed stain normalization and color augmentation strategies can handle the human level bias. But deep learning models can easily disentangle the linear transformation used in these approaches, resulting in undesirable bias and lack of generalization. To handle these limitations, we propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.","PeriodicalId":93349,"journal":{"name":"Pattern Recognition : ICPR International Workshops and Challenges, virtual event, January 10-15, 2021, proceedings. Part I. International Conference on Pattern Recognition (25th : 2021 : Online)","volume":"12661 ","pages":"120-140"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528268/pdf/nihms-1696243.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39554296","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}

引用次数: 18