An Interval-Valued Fuzzy Classifier Based on an Uncertainty-Aware Similarity Measure

Stachowiak, Anna; Żywica, Patryk; Dyczkowski, Krzysztof; Wójtowicz, Andrzej

doi:10.1007/978-3-319-11313-5_65

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…We have carried out research on uncertainty in ovarian tumor diagnosis and on the construction of classifiers on such data, and the results are very promising [22,23]. We have found that consideration of an uncertainty factor has a crucial influence on the final diagnosis.…”

Section: Motivationmentioning

confidence: 98%

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

Dyczkowski

Wójtowicz

Żywica

et al. 2015

Advances in Intelligent Systems and Computing

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Abstract. This article describes the fundamentals of an intelligent decision support system for the diagnosis of ovarian tumors. The system is designed to support diagnosis by less experienced gynecologists, and to gather data for continuous improvement of the quality of diagnosis. The theoretical basis for the construction of the system is the IF-sets framework, used to aggregate multiple decisionmaking methods, and simultaneously providing information about positive and negative diagnosis of a given tumor type.

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Section: Motivationmentioning

confidence: 98%

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

Dyczkowski

Wójtowicz

Żywica

et al. 2015

Advances in Intelligent Systems and Computing

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“…Dragiev et al [14] presented an uncertainty-aware approach to help an automated system in detecting grasping of humans. Here, the computation includes uncertainty-information which is utilized to refine the grasping detection.…”

Section: Uncertainty-aware Computer Visionmentioning

confidence: 99%

An Uncertainty-Aware Visual System for Image Pre-Processing

et al. 2018

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Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets.

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“…Our previous research indicated possible problems with the collection of data by a physician during examinations [15,16]. It is common that some examinations might be omitted by a gynaecologist, either due to their unavailability or because of medical reasons.…”

Section: Motivationmentioning

confidence: 99%

Improving medical decisions under incomplete data using interval–valued fuzzy aggregation

Żywica¹,

Wójtowicz²,

Stachowiak³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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We state a problem concerning how to make an effective and proper decision in the presence of data incompleteness. As an example we consider a medical diagnostic system where the problem of missing data is commonly encountered. We propose and evaluate an approach that makes it possible to reduce the influence of missing data on the final result and to improve the quality of the decision. The process involves interval-valued fuzzy set modelling, uncertaintification of classical methods, and finally aggregation of the incomplete results. It was verified that the aggregation results in meaningful and accurate decisions despite the missing data.

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An Interval-Valued Fuzzy Classifier Based on an Uncertainty-Aware Similarity Measure

Cited by 13 publications

References 21 publications

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

An Uncertainty-Aware Visual System for Image Pre-Processing

Improving medical decisions under incomplete data using interval–valued fuzzy aggregation

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