In the past few years a variety of successful algorithms to select/extract discriminative spectral bands was introduced. By exploiting the connectivity of neighbouring spectral bins, these techniques may be more beneficial than the standard feature selection/extraction methods applied for spectral classification. The goal of this paper is to study the effect of the training sample size on the performance of different strategies to select/extract informative spectral regions. We also consider the success of these methods compared to Principal Component Analysis (PCA) for different numbers of extracted components/groups of spectral bands.@en

Optical spectroscopy may be used for in vivo, noninvasive distinction of malignant from normal tissue. The aim of our study was to analyze the accuracy of various optical spectroscopic techniques for the classification of cancerous lesions of the bronchial tree. We developed a fiberoptic instrument allowing the measurement of autofluorescence spectroscopy (AFS), diffuse reflectance spectroscopy (DRS), and differential path length spectroscopy (DPS) during bronchoscopy. Spectroscopic measurements were obtained from 191 different endobronchial lesions (63 malignant and 128 nonmalignant) in 107 patients. AFS, DRS, and DPS sensitivity/specificity for the distinction between malignant and nonmalignant bronchial lesions were 73%/82%, 86%/81%, and 81%/88%, respectively. All three optical spectroscopic modalities facilitate an increase of the positive predictive value of autofluorescence bronchoscopy for the detection of endobronchial tumors. Even better results were obtained when the three spectroscopic techniques were combined. Key Words: autofluorescence bronchoscopy ¿ lung cancer ¿ optical spectroscopy@en

Abstract Autofluorescence spectroscopy and Raman spectroscopy have been suggested for lesion diagnostics. We investigate the information contained in autofluorescence and Raman spectra recorded from oral tissue slices of various lesion types. Thirty-seven human oral mucosa lesions were biopsied and freeze-dried. Complete autofluorescence images and spectra were recorded from 20 m sections. Raman spectra were acquired from the same positions for 12 of the sections. Cluster analysis was applied to find any relationship between spectral shape and lesion type or cell layer. Autofluorescence images showed high intensities for keratin layers and connective tissue, but hardly any for the epithelium. Autofluorescence spectra were centered around 520 nm and did not show specific spectral features. No clustering with regard to lesion type or cell layer was observed. Raman spectra allowed for reliable classification into cell layers, but differences between lesion types were not significant in this study. Autofluorescence spectra of freeze-dried oral mucosa sections did not contain useful information. A more comprehensive study is required for Raman spectra. Keywords Autofluorescence microspectroscopy - Raman microspectroscopy - Ex vivo - Oral cancer - Oral leukoplakia - Oral pathology@en

Detection of malignancies of the bronchial tree in an early stage, such as carcinoma in situ (CIS), augments the cure rate considerably. It has been shown that the sensitivity of autofluorescence bronchoscopy is better than white light bronchoscopy for the detection of CIS and dysplastic lesions. Autofluorescence bronchoscopy is, however, characterized by a low specificity with a high rate of false positive findings. In the present paper we propose to combine autofluorescence bronchoscopy with optical spectroscopy to improve the specificity of autofluorescence imaging, while maintaining the high sensitivity. Standard autofluorescence bronchoscopy was used to find suspect lesions in the upper bronchial tree, and these lesions were subsequently characterized spectroscopically using a custom made fiberoptic probe. Autofluorescence spectra of the lesions as well as reflectance spectra were measured. We will show in this preliminary report that the addition of either of these spectroscopic techniques decreases the rate of false positives findings, with the best results obtained when both spectroscopic modalities are combined. Keywords: Autofluorescence spectroscopy; Reflectance spectroscopy; Lung cancer; Bronchoscopy@en

Keywords autofluorescence spectroscopy ¿ cancer detection ¿ combined classifiers ¿ oral cancer ¿ reflectance spectroscopy Abstract Background and Objectives Autofluorescence and diffuse reflectance spectroscopy have been used separately and combined for tissue diagnostics. Previously, we assessed the value of autofluorescence spectroscopy for the classification of oral (pre-)malignancies. In the present study, we want to determine the contributions of diffuse reflectance and autofluorescence spectroscopy to diagnostic performance. Study Design/Materials and Methods Autofluorescence and diffuse reflectance spectra were recorded from 172 oral lesions and 70 healthy volunteers. Autofluorescence spectra were corrected in first order for blood absorption effects using diffuse reflectance spectra. Principal Components Analysis (PCA) with various classifiers was applied to distinguish (1) cancer and (2) all lesions from healthy oral mucosa, and (3) dysplastic and malignant lesions from benign lesions. Autofluorescence and diffuse reflectance spectra were evaluated separately and combined. Results The classification of cancer versus healthy mucosa gave excellent results for diffuse reflectance as well as corrected autofluorescence (Receiver Operator Characteristic (ROC) areas up to 0.98). For both autofluorescence and diffuse reflectance spectra, the classification of lesions versus healthy mucosa was successful (ROC areas up to 0.90). However, the classification of benign and (pre-)malignant lesions was not successful for raw or corrected autofluorescence spectra (ROC areas@en

In combining classifiers, it is believed that diverse ensembles perform better than non-diverse ones. In order to test this hypothesis, we study the accuracy and diversity of ensembles obtained in bagging and boosting applied to the nearest mean classifier. In our simulation study we consider two diversity measures: the statistic and the disagreement measure. The experiments, carried out on four data sets have shown that both diversity and the accuracy of the ensembles depend on the training sample size. With exception of very small training sample sizes, both bagging and boosting are more useful when ensembles consist of diverse classifiers. However, in boosting the relationship between diversity and the efficiency of ensembles is much stronger than in bagging.@en

Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented. Keywords: Bagging; Boosting; Combining classifiers; Linear classifiers; Random subspaces; Training sample size@en

In classifier combining, one tries to fuse the information that is given by a set of base classifiers. In such a process, one of the difficulties is how to deal with the variability between classifiers. Although various measures and many combining rules have been suggested in the past, the problem of constructing optimal combiners is still heavily studied. In this paper, we discuss and illustrate the possibilities of classifier embedding in order to analyse the variability of base classifiers, as well as their combining rules. Thereby, a space is constructed in which classifiers can be represented as points. Such a space of a low dimensionality is a Classifier Projection Space (CPS). In the first instance, it is used to design a visual tool that gives more insight into the differences of various combining techniques. This is illustrated by some examples. In the end, we discuss how the CPS may also be used as a basis for constructing new combining rules.@en

Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented. Key words: Bagging; Boosting; Combining classifiers; Linear classifiers; Random subspaces; Training sample size@en