The Reliability Issue of Computer-Aided Breast Cancer Diagnosis

Kovalerchuk, Boris; Triantaphyllou, Evangelos; Ruiz, James F.; Torvik, Vetle I.; Vityaev, Evgenii

doi:10.1006/cbmr.2000.1546

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…Although physicians can rely increasingly on computer-assisted diagnosis (Golobardes et al 2002;Kovalerchuk et al 2000), including for some cancers, this is not the case for gynecological cancers. Accordingly, a physician must make a decision based on a medical history elicited for clues of possible causes of anomaly, clinical examination, and the results of investigations such as blood tests, laboratory analysis of human tissues and fluids, x-rays, and ultrasound.…”

Section: Toward a Typology Of Delay: The Search For Gynecological Canmentioning

confidence: 99%

Embodied Changes and the Search for Gynecological Cancer Diagnosis

Marković

2004

Med Anthropol Q

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The detection and successful treatment of cancers is dependent on timely presentation with abnormal and often subtle symptoms. In this article, we draw on research conducted with Australian immigrant women in 2001-02 who experienced delays in diagnosis of gynecological cancer. Data from in-depth interviews with women with gynecological cancer indicated a common trajectory of an "illness career" whereby the search for diagnosis was often painful and lengthy, either because women normalized the abnormal signs or because their experiences of bodily abnormality, pain, and dysfunction contrasted with medical explanations. This delay was sometimes exacerbated by structural barriers. As a result, diagnosis was often protracted, during which time women presented with symptoms that were dismissed or resulted in the misdiagnosis, with a resultant loss of confidence in clinical services.

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Section: Toward a Typology Of Delay: The Search For Gynecological Canmentioning

confidence: 99%

Embodied Changes and the Search for Gynecological Cancer Diagnosis

Marković

2004

Med Anthropol Q

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“…The details of that decomposition are left out for the purpose of simplifying this illustration. The interested reader is referred to [13,14] for more details. It should also be noted that the two functions were both inferred from querying the radiologist.…”

Section: An Application Of Problem #1 To Breast Cancer Diagnosismentioning

confidence: 99%

Guided inference of nested monotone Boolean functions

Torvik

Triantaphyllou

2003

Information Sciences

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This paper addresses the problem of minimizing the average query complexity of inferring a pair of nested monotone Boolean functions defined on f0; 1g n using a pair of oracles. Here, nested refers to the case when one of the functions is always greater than or equal to the other function. It is shown that the nested case is equivalent to inferring the single function case defined on f0; 1g nþ1 when access to the two oracles is unrestricted. Two common examples of restricted oracles, namely sequential oracles and a single three-valued oracle, are also analyzed. The most efficient known approach to minimizing the average query complexity in inferring a single monotone Boolean function is based on a query selection criterion. It is shown that the selection criterion approach is easily modified for use with restricted oracles. Several real world examples illustrate the necessity and sufficiency of the nested monotone Boolean function model. Extensive computational results indicate that the nestedness assumption reduces the average query complexity by a few percent. This is a dramatic improvement considering the fact that this complexity is exponential in n.

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“…A framework for classification of knowledge management system (Sabherwal and Becerra-Fernandez, 2005) defines systems for knowledge discovery in this typology (other such systems include knowledge preservation, knowledge application, and knowledge repositories). While KDD systems have made a significant contribution in scientific research fields for years, for example in breast cancer diagnosis (Kovalerchuck et al, 2000). Recently, KDD is extensively applied from determining a customer's likelihood of purchasing a new product (e.g., Bank of Montreal), to discovering how to evenly prune the inventory of flowers by downplaying better selling products on the web page, to website customisation through usage mining by making assumptions about a customer based on their ZIP code information, and the hotel management with Knowledge discovery (Choi and Cho, 2000).…”

Section: Introductionmentioning

confidence: 99%

Power load forecasting using data mining and knowledge discovery technology

Wang

Niu

2011

IJIIDS

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Considering the importance of the peak load to the dispatching and management of the electric system, the error of peak load is proposed in this paper as criteria to evaluate the effect of the forecasting model. This paper proposes a systemic framework that attempts to use data mining and knowledge discovery (DMKD) to pretreat the data. And a new model is proposed which combines artificial neural networks with data mining and knowledge discovery for electric load forecasting. With DMKD technology, the system not only could mine the historical daily loading which had the same meteorological category as the forecasting day to compose data sequence with highly similar meteorological features, but also could eliminate the redundant influential factors. Then an artificial neural network is constructed to predict according to its characteristics. Using this new model, it could eliminate the redundant information, accelerate the training speed of neural network and improve the stability of the convergence. Compared with single BP neural network, this new method can achieve greater forecasting accuracy.

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The Reliability Issue of Computer-Aided Breast Cancer Diagnosis

Cited by 16 publications

References 33 publications

Embodied Changes and the Search for Gynecological Cancer Diagnosis

Embodied Changes and the Search for Gynecological Cancer Diagnosis

Guided inference of nested monotone Boolean functions

Power load forecasting using data mining and knowledge discovery technology

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