Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm

Chang, Yuan‐Hsiang; Wang, Xiaohui; Good, Walter F.; Gur, David

doi:10.1016/s1076-6332(99)80226-8

Cited by 62 publications

(29 citation statements)

References 18 publications

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“…The local features are continuous numbers, which must be segmented into discrete states. Based on our experimental results, which had been reported elsewhere [28], all local features were segmented into five discrete states. Using the range of values for each and total 2,492 training regions (288 true-positive and 2,204 false-positive regions in the training database), the segmentation boundaries were determined with the criterion that all states contained approximately the same number of regions (approximately 498).…”

Section: Topology Of a Bayesian Belief Networkmentioning

confidence: 99%

<title>Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection</title>

et al. 1999

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The study is to investigate the use of a Bayesian belief network (BBN) in a computer-assisted diagnosis (CAD) scheme for mass detection in digitized mammograms. Two independent image sets were used in the experiments. After initial processing of image segmentation and adaptive topographic region growth in our CAD scheme, 288 true-positive mass regions and 2,204 false-positive regions were identified in the training image set. In the testing set, 304 true-positive and 1,586 false-positive regions were identified. Fifty features were computed for each region. After using a genetic algorithm search, a BBN was constructed based on 12 local and four global features in order to classify these regions as positive or negative for mass. The performance of the BBN was evaluated using an ROC methodology. The BBN achieved an area under the ROC curve of 0.873 0.009 in classifying the 304 positive and 1,586 negative regions in the testing set. This result was better than using an artificial neural network with the same set of input features. After incorporating the BBN into our CAD scheme as the last classification stage, we detected 80% of 189 positive mass cases (in 433 testing images) with an average detection rate of 0.76 false-positive regions per image. Therefore, this study demonstrated that a BBN approach could yield a comparable performance to that using other classifiers. Using a probabilistic learning concept and interpretable topology, the BBN provides a flexible approach to improving CAD schemes.

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Section: Topology Of a Bayesian Belief Networkmentioning

confidence: 99%

<title>Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection</title>

et al. 1999

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“…Genetic algorithms have already been used in medicine, mainly for image segmentation tasks, but also for feature selection and model optimization (13,14,15).…”

Section: Introductionmentioning

confidence: 99%

Final year medical students’ understanding of family medicine

Šter

Švab

Šter

2014

AMA

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Objective. e European Academy of Teachers in General Practice / Family Medicine (EURACT) has developed an educational agenda, the key document for teaching family medicine in Europe. e aim of our study was to nd out how nal year medical students at the beginning of their family medicine clerkship understand the discipline of family medicine. Methods. e attitudes toward family medicine were paraphrased and developed into a 164-item questionnaire, which was administered to 335 nal-year medical students at the beginning of their clerkship. Using combinatorial optimization with genetic algorithms we selected 30 items which yielded the highest Cronbach alpha reliability coe cient. Finally, we performed a factor analysis to nd which dimensions of family medicine were recognised by the students and compared them with the domains de ned in the EURACT de nition. Results. e 30-item questionnaire had a Cronbach alpha reliability coe cient of 0.919. e di erences between male and female students were not very signi cant (p=0.061). With the factor analysis we recognised seven factors, belonging to three out of six domains of the EURACT educational agenda: primary care management, personcenteredness and comprehensive approach. Conclusion. Final-year medical students at the beginning of their family medicine clerkship understand some of the dimensions of family medicine rather well, but they are not aware of some important competences of family doctors. ere is a necessity to teach students about speci c problem solving skills and the importance of balance between the health needs of an individual patient and the community.

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“…Such algorithms have been extensively used outside medicine in applications such as data fitting, scheduling, trend spotting, and budgeting (11 -13). They have been evaluated in medicine in breast cancer diagnosis (14) and in an antiviral in vitro model assessing a large space of dose permutations of a few cocktails (15). Here, a well-established strategy is applied to a novel setting: the discovery of drug cocktails.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic guided screening of drug combinations of arbitrary size for activity against cancer cells

Zinner¹,

Barrett²,

Popova³

et al. 2009

Molecular Cancer Therapeutics

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The standard treatment for most advanced cancers is multidrug therapy. Unfortunately, combinations in the clinic often do not perform as predicted. Therefore, to complement identifying rational drug combinations based on biological assumptions, we hypothesized that a functional screen of drug combinations, without limits on combination sizes, will aid the identification of effective drug cocktails. Given the myriad possible cocktails and inspired by examples of search algorithms in diverse fields outside of medicine, we developed a novel, efficient search strategy called Medicinal Algorithmic Combinatorial Screen (MACS). Such algorithms work by enriching for the fitness of cocktails, as defined by specific attributes through successive generations. Because assessment of synergy was not feasible, we developed a novel alternative fitness function based on the level of inhibition and the number of drugs. Using a WST-1 assay on the A549 cell line, through MACS, we screened 72 combinations of arbitrary size formed from a 19-drug pool across four generations. Fenretinide, suberoylanilide hydroxamic acid, and bortezomib (FSB) was the fittest. FSB performed up to 4.18 SD above the mean of a random set of cocktails or ''too well'' to have been found by chance, supporting the utility of the MACS strategy. Validation studies showed FSB was inhibitory in all 7 other NSCLC cell lines tested. It was also synergistic in A549, the one cell line in which this was evaluated. These results suggest that when guided by MACS, screening larger drug combinations may be feasible as a first step in combination drug discovery in a relatively small number of experiments. [Mol Cancer Ther 2009;8(3):521 -32]

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Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm

Cited by 62 publications

References 18 publications

<title>Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection</title>

<title>Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection</title>

Final year medical students’ understanding of family medicine

Algorithmic guided screening of drug combinations of arbitrary size for activity against cancer cells

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