Print Email Facebook Twitter Design and Interpretation of a Convolutional Neural Network Architecture for Imaging Mass Spectrometry Data Title Design and Interpretation of a Convolutional Neural Network Architecture for Imaging Mass Spectrometry Data Author CHANOPOULOU, IOANNA (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Van de Plas, Raf (mentor) Tideman, L.E.M. (graduation committee) Soloviev, O.A. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2023-07-12 Abstract Convolutional Neural Networks (CNNs) have emerged primarily from research focusing on image classification tasks and as a result, most of the well-motivated design choices found in literature are relevant to computer vision applications. CNNs' application on Imaging Mass Spectrometry (IMS) data is quite recent and involves new challenges, such as taking into account their unique structure (e.g. both spatial and spectral dimensions). In this thesis, we suggest a 1-D CNN architecture that extracts local features along the spectral dimension. The aim is to investigate if CNNs improve the classification accuracy compared to other classic Machine Learning (ML) methods such as linear models. Furthermore, we explore Neural Networks (NNs) that employ the novel Sharpened Cosine Similarity (SCS) as a feature extraction method, opposed to convolution. We call those networks SCS-NN in correspondence to the Convolutional-NN (CNN). To evaluate these methods, we implement our pipeline for various IMS datasets, with different characteristics and classification tasks, using several performance metrics such as balanced accuracy and F1 score.Moreover, we provide a detailed description of the methodology pipeline used for the CNN architecture design. The suggested methodology is the Tree-structured Parzen Estimator (TPE) algorithm, a Bayesian optimization technique for automated architecture selection. By implementing TPE, we manage to explore and exploit efficiently a complex and large hyperparameter configuration space and automatically select optimal hyperparameters (such as number of convolutional layers, kernel size, strides, learning rates etc.). This automated approach reduces time consumption, errors, and the need for specialized knowledge in biology and biochemistry that would be associated with manual design. In addition to developing a pipeline for designing, training and evaluating a CNN for IMS data classification, we also apply a model agnostic interpretation methodology based on SHapley Additive exPlanations (SHAP) and provide SHAP score maps that visualize the importance of features in the spatial dimension of the IMS datacube.In this thesis, we present and analyse the automated selection of 1-D CNN architectures for IMS data classification based on the TPE algorithm. Furthermore, we investigate a novel alternative to convolution, SCS, and evaluate its strengths and weaknesses in IMS data classification. The experimental results show that the TPE-generated CNN architectures outperform all the other applied classifiers. Finally, our interpretation of the CNN models reveals that accuracy performance alone might not be a sufficient criterion to trust the model's output. Subject imaging mass spectrometryConvolutional Neural Networks (CNNs)Sharpened Cosine Similarity (SCS)supervised machine learningdeep learninginterpretabilitySHAP To reference this document use: http://resolver.tudelft.nl/uuid:5aa31b80-7b80-437e-b7f7-3c89aae17738 Part of collection Student theses Document type master thesis Rights © 2023 IOANNA CHANOPOULOU Files PDF MScThesis_I_Chanopoulou.pdf 11.9 MB Close viewer /islandora/object/uuid:5aa31b80-7b80-437e-b7f7-3c89aae17738/datastream/OBJ/view