MOTIVATION: Non-negative matrix factorization (NMF) is a common tool for obtaining low-rank approximations of non-negative data matrices and has been widely used in machine learning, e.g. for supporting feature extraction in high-dimensional classification tasks. In its classical form, NMF is an unsupervised method, i.e. the class labels of the training data are not used when computing the NMF. However, incorporating the classification labels into the NMF algorithms allows to specifically guide them toward the extraction of data patterns relevant for discriminating the respective classes. This approach is particularly suited for the analysis of mass spectrometry imaging (MSI) data in clinical applications, such as tumor typing and classification, which are among the most challenging tasks in pathology. Thus, we investigate algorithms for extracting tumor-specific spectral patterns from MSI data by NMF methods. RESULTS: In this article, we incorporate a priori class labels into the NMF cost functional by adding appropriate supervised penalty terms. Numerical experiments on a MALDI imaging dataset confirm that the novel supervised NMF methods lead to significantly better classification accuracy and stability as compared with other standard approaches. AVAILABILITY AND IMPLEMENTATON: https://gitlab.informatik.uni-bremen.de/digipath/Supervised_NMF_Methods_for_MALDI.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Results: Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. Availability and implementation: https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

An analysis of spatial context conditioning and run-length coding in embedded progressive, wavelet-based image coding is presented. The analysis shows that run-length coding of certain context subsequences is superior to pure entropy coding, both in terms of coding performance and of execution speed. Based on these considerations, a novel, intuitive context conditioning scheme using a spatial distance model to describe the statistics of significant coefficients is proposed. The results for the proposed coding scheme are competitive to the best coding schemes found in the literature.

Run-length coding has attracted much attention in wavelet-based image compression because of its simplicity and potentially low complexity. The main drawback is the inferior RD-performance compared to the state-of-the-art-coder SPIHT. In this paper, we concentrate on the embedded progressive run-length code of Tian and Wells (1996, 1998). We consider significance sequences drawn from the scan in the dominant pass. It turns out that self-similar curves for scanning the dominant pass increase the compression efficiency significantly. This is a consequence of the correlation of direct neighbors in the wavelet domain. This dependence can be better exploited by using groups of coefficients, similar to the SPIHT algorithm. This results in a new and very fast coding algorithm, which shows performance similar to the state-of-the-art coder SPIHT, but with lower complexity and small and fixed memory overhead.

The problem of correcting the baseline distortion of signals measured in magnetic resonance spectroscopy is investigated in the context of Wiener filter theory. Based on the estimation of model parameters describing the spectrum, a method is proposed that estimates the correlation operators of the net signal and the baseline distortion. These operators are used to construct a Wiener filter for the measured spectrum. Results of numerical simulations that compare the proposed method with other methods are given.