Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium最新文献

Identifying Relation-gaps in Ontologies using TOPSIS 使用TOPSIS识别本体中的关系缺口

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2022-01-01 DOI: 10.32473/flairs.v35i.130605

S. Subhashree, P. S. Kumar

引用次数: 0

ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization ColorNephroNet:利用医学图像着色预测肾肿瘤恶性

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2022-01-01 DOI: 10.32473/flairs.v35i.130689

Aleksander Obuchowski, Barbara Klaudel, Roman Karski, Bartosz Rydziński, Mateusz Glembin, Paweł Syty, P. Jasik

{"title":"ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization","authors":"Aleksander Obuchowski, Barbara Klaudel, Roman Karski, Bartosz Rydziński, Mateusz Glembin, Paweł Syty, P. Jasik","doi":"10.32473/flairs.v35i.130689","DOIUrl":"https://doi.org/10.32473/flairs.v35i.130689","url":null,"abstract":"Renal tumor malignancy classification is one of the cru- cial tasks in urology, being a primary factor included in the decision of whether to perform kidney removal surgery (nephrectomy) or not. Currently, tumor malignancy prediction is determined by the radiological di- agnosis based on computed tomography (CT) images. However, it is estimated that up to 16% of nephrec- tomies could have been avoided because the tumor that had been diagnosed as malignant, was found to be be- nign in the postoperative histopathological examination. The excess of false-positive diagnoses results in unnec- essarily performed nephrectomies that carry the risk of periprocedural complications. In this paper, we present a machine-aided diagnosis system that predicts the tumor malignancy based on a CT image. The prediction is performed after radiological diagnosis and is used to capture false-positive diagnoses. Our solution is able to achieve a 0.84 F1-score in this task. We also propose a novel approach to knowledge transfer in the medical do- main in terms of colorization based pre-processing that is able to increase the F1-score by up to 1.8pp.","PeriodicalId":88964,"journal":{"name":"Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89636164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clustering Partial Lexicographic Preference Trees 聚类部分字典偏好树

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2021-05-18 DOI: 10.1609/aaai.v35i18.17872

Joseph Allen, Xudong Liu, K. Umapathy, S. Reddivari

引用次数: 0

Partial (Neighbourhood) Singleton Arc Consistency for Constraint Satisfaction Problems 约束满足问题的部分(邻域)单弧一致性

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2020-09-28 DOI: 10.3233/FI-2020-1944

R. Wallace

{"title":"Partial (Neighbourhood) Singleton Arc Consistency for Constraint Satisfaction Problems","authors":"R. Wallace","doi":"10.3233/FI-2020-1944","DOIUrl":"https://doi.org/10.3233/FI-2020-1944","url":null,"abstract":"Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP are chosen to be made SAC-consistent or neighbourhood-SAC-consistent. Such consistencies, despite their partial character, are still well-characterized in that algorithms have unique fixpoints. Heuristic strategies for choosing an effective subset of variables are described and tested, the best being choice by highest degree and a more complex strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as the corresponding full version of the algorithm in terms of values discarded or problems proven unsatisfiable, while significantly reducing the effort required to achieve this.","PeriodicalId":88964,"journal":{"name":"Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium","volume":"86 1","pages":"140-145"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80990642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Gazetteer Generation for Neural Named Entity Recognition 用于神经命名实体识别的地名词典生成

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2020-05-17 DOI: 10.13016/M2HPFL-WYOV

Chan Hee Song, Dawn J Lawrie, Tim Finin, J. Mayfield

引用次数: 6

Representation of Indifference and Preference Ordering Between Random Variables by Coherent Upper and Lower Conditional Previsions Defined by Hausdorff Outer and Inner Measures 由Hausdorff内外测度定义的连贯上下条件前提表示随机变量间的无差异和偏好排序

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2020-01-01 DOI: 10.1007/978-3-642-15956-5_9

S. Doria

引用次数: 5

Active Learning of Multi-Class Classifiers with Auxiliary Probabilistic Information. 基于辅助概率信息的多类分类器主动学习。

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2018-05-01

Yanbing Xue, Milos Hauskrecht

引用次数: 0

Multivariate Conditional Outlier Detection: Identifying Unusual Input-Output Associations in Data. 多变量条件离群点检测：识别数据中不寻常的输入输出关联。

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2018-05-01

Charmgil Hong, Milos Hauskrecht

引用次数: 0

Group-Based Active Learning of Classification Models. 基于分组的分类模型主动学习。

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2017-05-01

Zhipeng Luo, Milos Hauskrecht

引用次数: 0

Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking. 基于分类和排序的软标签分类模型高效学习。

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium Pub Date : 2017-05-01

Yanbing Xue, Milos Hauskrecht

{"title":"Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking.","authors":"Yanbing Xue, Milos Hauskrecht","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Construction of classification models from data in practice often requires additional human effort to annotate (label) observed data instances. However, this annotation effort may often be too costly and only a limited number of data instances may be feasibly labeled. The challenge is to find methods that let us reduce the number of the labeled instances but at the same time preserve the quality of the learned models. In this paper we study the idea of learning classification from soft label information in which each instance is associated with a soft-label further refining its class label. One caveat of applying this idea is that soft-labels based on human assessment are often noisy. To address this problem, we develop and test a new classification model learning algorithm that relies on soft-label binning to limit the effect of soft-label noise. We show this approach is able to learn classification models more rapidly and with a smaller number of labeled instances than (1) existing soft label learning methods, as well as, (2) methods that learn from class-label information.</p>","PeriodicalId":88964,"journal":{"name":"Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium","volume":"2017 ","pages":"164-169"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512716/pdf/nihms873697.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35184452","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: 0