Dissertationen zum Thema „Radiomics analysis“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-23 Dissertationen für die Forschung zum Thema "Radiomics analysis" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Dissertationen für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Xu, Chongrui. „Quantitative Radiomic Analysis for Prognostic Medical Applications“. Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.
Der volle Inhalt der QuelleOrtiz, Ramón Rafael. „Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging“. Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/119118.
Der volle Inhalt der Quelle[CAT] En els últims anys, els investigadors han intentat explotar la informació de les imatges mèdiques a través de l'avaluació de nombrosos paràmetres quantitatius per ajudar els clínics amb el diagnòstic i la valoració de malalties. Aquesta pràctica ha sigut batejada com radiomics,. L'anàlisi de textures proporciona una gran varietat de paràmetres que permeten quantificar l'heterogeneïtat característica de diferents teixits, especialment quan s'obtenen a partir d'imatge per ressonància magnètica (IRM). Basant-nos en aquests fets, vam decidir estudiar les possibilitats dels paràmetres texturals extrets d'IRM per caracteritzar diversos trastorns cerebrals. El potencial de les textures es va analitzar amb mètodes d'aprenentatge automàtic, usant diferents classificadors i mètodes de selecció de característiques per trobar el model òptim per a cada tasca específica. En aquesta tesi, la metodologia radiomics es va emprar per realitzar quatre projectes independents. En el primer projecte, vam estudiar la diferenciació entre glioblastomes multiformes (GBMs) i metàstasis cerebrals (MCs) en IRM convencional. Aquests tipus de tumors cerebrals poden confondre's al diagnosticar-se ja que presenten un perfil radiològic similar i les dades clíniques poden no ser concloents. Per tal d'evitar procediments exhaustius i invasius, vam estudiar el poder discriminatori de textures 2D extretes d'imatges de referència T1 filtrades i sense filtrar. Els resultats suggereixen que els paràmetres texturals proporcionen informació sobre l'heterogeneïtat dels GBMs i les MCs que pot servir per distingir amb precisió ambdues lesions quan s'utilitza una aproximació d'aprenentatge automàtic adequada. En el segon projecte, vam analitzar la classificació de MCs segons el seu origen primari en IRM de referència. En un percentatge de pacients, les MCs són diagnosticades com la primera manifestació d'un tumor primari desconegut. Per tal de detectar el tumor primari d'una forma no invasiva i més ràpida, vam examinar la capacitat de l'anàlisi de textura 2D i 3D per diferenciar les MCs derivades dels tumors primaris més propensos a metastatitzar (càncer de pulmó, càncer de mama i melanoma) en imatges T1. Els resultats van mostrar que s'aconsegueix una alta precisió quan s'utilitza un conjunt reduït de textures 3D per diferenciar les MCs de càncer de pulmó de les MCs de càncer de mama i melanoma. En el tercer projecte, vam avaluar les propietats de l'hipocamp en la IRM per identificar les diferents etapes de la malaltia d'Alzheimer (MA). Els criteris actuals per diagnosticar la MA requereixen la presència de dèficits cognitius severs. Amb la idea d'establir nous biomarcadors per detectar la MA en les seues primeres etapes, vam avaluar un conjunt de textures 2D i 3D extretes d'IRM de l'hipocamp de pacients amb MA avançada, deteriorament cognitiu lleu i normalitat cognitiva. Molts paràmetres de textura 3D van resultar ser estadísticament significatius per diferenciar entre pacients amb MA i individus de les altres dues poblacions. En combinar aquests paràmetres amb tècniques d'aprenentatge automàtic, es va obtenir una alta precisió. En el quart projecte, vam intentar caracteritzar els patrons d'heterogeneïtat de l'ictus cerebral isquèmic en la IRM estructural. En la IRM cerebral d'individus d'edat avançada, alguns processos patològics presenten característiques similars, com les lesions per ictus i les hiperintensitats de la substància blanca (HSBs). Atès que els ictus tenen efecte també en teixit adjacent, vam decidir estudiar la viabilitat de textures 3D extretes de les HSBs, la substància blanca no afectada i les estructures subcorticals per diferenciar individus afectats per ictus llacunars o corticals visibles en IRM convencional (imatges T1, T2 i FLAIR) d'individus sense ictus. Les textures no foren útils per diferenciar ictus corticals i llacunars, però es van obtenir resultats prometedors per disce
[EN] Over the last years, researchers have attempted to exploit the information provided by medical images through the evaluation of numerous imaging quantitative parameters in order to help clinicians with the diagnosis and assessment of many lesions and diseases. This practice has been recently named as radiomics. Texture analysis supply a wide range of features that allow quantifying the distinctive heterogeneity of different tissues, especially when obtained from magnetic resonance imaging (MRI). With this in mind, we decided to study the possibilities of texture features from MRI in order to characterize several disorders that affect the human brain. The potential of texture features was analyzed with various machine learning approaches, involving different classifiers and feature selection methods so as to find the optimal model to accomplish each specific task. In this thesis, the radiomics methodology was used to perform four independent projects. In the first project, we studied the differentiation between glioblastomas (GBMs) and brain metastases (BMs) in conventional MRI. Sometimes these types of brain tumors can be misdiagnosed since they may present a similar radiological profile and the clinical data may be inconclusive. With the aim of avoiding exhaustive and invasive procedures, we studied the discriminatory power of a large amount of 2D texture features extracted from baseline original and filtered T1-weighted images. The results suggest that 2D texture features provide some heterogeneity information of GBMs and BMs that can help in their accurate discernment when using the proper machine learning approach. In the second project, we analyzed the classification of BMs by their primary site of origin in baseline MRI. A percentage of patients are diagnosed with BM as the first manifestation of an unknown primary tumor. In order to detect the primary tumor in a faster non-invasive way, we examined the capability of 2D and 3D texture analysis to differentiate BMs derived from the most common primary tumors (lung cancer, breast cancer and melanoma) in T1-weighted images. The results showed that high accuracy was achieved when using a reduced set of 3D descriptors to differentiate lung cancer BMs from breast cancer and melanoma BMs. In the third project, we evaluated the hippocampus MRI profile of Alzheimer's disease (AD) patients to identify the different stages of the disease. The current criteria for diagnosing AD require the presence of relevant cognitive deficits. With the purpose of establishing new biomarkers to detect AD in its early stages, we evaluated a set of 2D and 3D texture features extracted from MRI scans of the hippocampus of patients with advanced AD, early mild cognitive impairment and cognitive normality. Many 3D texture parameters resulted to be statistically significant to differentiate between AD patients and subjects from the other two populations. When combining these 3D parameters with machine learning techniques, high accuracy was obtained. In the fourth project, we attempted to characterize the heterogeneity patterns of ischemic stroke in structural MRI. In brain MRI of older individuals, some pathological processes present similar imaging characteristics, like in the case of stroke lesions and white matter hyperintensities (WMH) of diverse natures. Given that stroke effects are present not only in the affected region, but also in unaffected tissue, we investigated the feasibility of 3D texture features from WMH, normal-appearing white matter and subcortical structures to differentiate individuals who had a lacunar or cortical stroke visible on conventional brain MRI (T1-weighted, T2-weighted and FLAIR images) from subjects who did not. Texture features were not useful to differentiate between post-acute cortical and lacunar strokes, but promising results were achieved for discerning between patients presenting an old stroke and normal-ageing patients who never had a stroke.
Ortiz Ramón, R. (2019). Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/119118
TESIS
Iyer, Sukanya Raj. „Deformation heterogeneity radiomics to predict molecular sub-types and overall survival in pediatric Medulloblastoma“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1588601774292049.
Der volle Inhalt der QuelleWang, Dingqian. „Quantitative analysis with machine learning models for multi-parametric brain imaging data“. Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22245.
Der volle Inhalt der QuelleBoughdad, Sarah. „Contributions of radiomics in ¹⁸F-FDG PET/CT and in MRI in breast cancer“. Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS500.
Der volle Inhalt der QuelleBreast cancer is a common disease for which ¹⁸F-FDG PET/CT and breast MRI are frequently performed in routine practice. However, the different information provided by each of these imaging techniques are currently under-exploited. Indeed, in routine the interpretation of these scans is mainly based on visual analysis whereas the « quantitative » analysis of PET/CT data is generally limited to the sole use of the SUVmax while in breast MRI, simple parameters to characterize tumor enhancement after injection of contrast medium are used. The advent of PET/MRI machines, calls for an evaluation of the contribution of a more advanced quantification of each of the modalities separately and in combination in the setting of breast cancer. This is along with the concept of « Radiomics » a field currently expanding and which consists in extracting many quantitative characteristics from medical images used in clinical practice to decipher tumor heterogeneity or improve prediction of prognosis. The aim of our work was to study the contribution of radiomic data extracted from ¹⁸F-FDG PET and MRI imaging with contrast injection to characterize tumor heterogeneity in breast cancer taking into account the different molecular subtypes of breast cancer, namely luminal (Lum A, Lum B HER2- and Lum B HER2 +), triple-negative and HER2 + tumors. In this context, we focused on the prediction of prognosis in patients treated with neo-adjuvant chemotherapy. The influence of physiological variations such as age on the calculation of radiomic data in normal breast and breast tumors separately was also explored, as well as the multi-center variability of radioman features. Radiomic features were extracted using the LiFex software developed within IMIV laboratory. The patient database used for the studies were all retrospective data. We reported for the first time the influence of age on the values of radiomic features in healthy breast tissue in patients recruited from 2 different institutions but also in breast tumors especially those with a triple-negative subtype. Similarly, significant associations between the radiomic tumor phenotype in PET and MRI imaging and well-established prognostic factors in breast cancer have been identified. In addition, we showed a large variability in the PET « radiomic profile » of breast tumors with similar breast cancer subtype suggesting complementary information within their metabolic phenotype defined by radiomic features. Moreover, taking into account this variability has been shown to be of particular interest in improving the prediction of pathological response in patients with triple-negative tumors treated with neoadjuvant chemotherapy. A peri-tumoral breast tissue region satellite to the breast tumor was also investigated and appeared to bear some prognostic information in patients with Lum B HER2- tumors treated with neoadjuvant chemotherapy. In MR, we demonstrated the need to harmonize the methods for radiomic feature calculation. Overall, we observed that radiomic features derived from MR were less informative about the molecular features of the tumors than radiomic features extracted from PET data and were of lower prognostic value. Yet, the combination of the enhanced tumor volume in MR with a PET radiomic feature and the tumor molecular subtype yielded enhanced the accuracy with which response to neoadjuvant therapy could be predicted compared to features from one modality only or molecular subtype only
Mahon, Rebecca N. „Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty“. VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5516.
Der volle Inhalt der QuelleOliver, Jasmine Alexandria. „Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis“. Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6123.
Der volle Inhalt der QuellePrasanna, Prateek. „NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.
Der volle Inhalt der QuelleChirra, Prathyush V. Chirra. „EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.
Der volle Inhalt der QuelleBasu, Satrajit. „Developing Predictive Models for Lung Tumor Analysis“. Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/3963.
Der volle Inhalt der QuelleSpagnoli, Lorenzo. „COVID-19 prognosis estimation from CAT scan radiomics: comparison of different machine learning approaches for predicting patients survival and ICU Admission“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23926/.
Der volle Inhalt der QuelleCaptier, Nicolas. „Multimodal analysis of radiological, pathological, and transcriptomic data for the prediction of immunotherapy outcome in Non-Small Cell Lung Cancer patients“. Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS012.
Der volle Inhalt der QuelleOverall survival of patients with metastatic non-small cell lung cancer (NSCLC) has been increasing with the use of anti-PD-1 immune checkpoint inhibitors. However, the duration of response remains highly variable between patients, and only 20-30% of patients are alive at 2 years. Thus, new biomarkers for predicting response to treatment and patient outcomes are still needed to guide therapeutic decision. In my PhD, we investigated machine learning approaches to leverage radiological, transcriptomic, and pathological data, integrating them into powerful multimodal models that might improve the limited predictive power of routine clinical data.My doctoral research stood at the heart of a multidisciplinary project funded by Fondation ARC call «SIGN’IT 2020—Signatures in Immunotherapy». It brought together several research teams of Institut Curie alongside a team from Institut du thorax, led by Professor Nicolas Girard, in charge of patient management and data collection. We built a new multimodal cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy alone or combined with chemotherapy. At baseline, we collected clinical information from routine care, 18F-FDG PET/CT scans, digitized pathological slides from the initial diagnosis, and bulk RNA-seq profiles from solid biopsies. Immunotherapy outcome was monitored with Overall Survival (OS) and Progression-Free Survival (PFS).Together with Irène Buvat and Emmanuel Barillot, whose teams hold significant expertise in the analysis of medical images and RNAseq tumor profiles, respectively, we initially focused on designing computational tools to extract relevant and interpretable information from these two data modalities. We notably developed a Python tool to apply Independent Component Analysis (ICA) on omics data and stabilize the results through multiple runs. We then explored the potential of stabilized ICA to extract powerful and biologically relevant transcriptomic features for the prediction of patient outcome. For medical images, and in particular 18F-FDG PET scans, we investigated the potential of radiomic approaches to characterize the metastatic disease at the whole-body level and design novel predictive features. We designed a Python explanation tool, based on Shapley values, to highlight the contribution of each individual metastasis to the prediction of radiomic models that use as input such whole-body features. A substantial portion of my PhD was devoted to the integration of clinical, radiomic, and transcriptomic features, as well as pathomic features extracted from digitized pathological slides (with the assistance of Thomas Walter’s team). We conducted a thorough comparison of the predictive capabilities of the different multimodal combinations using various state-of-the-art learning algorithms and integration methods. We devised strategies to overcome the many challenges associated to multimodal integration within our dataset, including handling missing modalities for numerous patients, dealing with a modest cohort size in comparison to the high dimensionality of the data, or ensuring a fair comparison of all the possible multimodal combinations. We especially focused on highlighting the potential of multimodal approaches to enhance patient risk stratification with respect to models using only clinical information collected during routine care
Kakino, Ryo. „Quantitative image analysis for prognostic prediction in lung SBRT“. Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263582.
Der volle Inhalt der QuelleAntunes, Jacob T. Antunes. „Quantitative Treatment Response Characterization In Vivo: UseCases in Renal and Rectal Cancers“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1467987922.
Der volle Inhalt der QuellePerier, Cynthia. „Analyse quantitative des données de routine clinique pour le pronostic précoce en oncologie“. Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0219/document.
Der volle Inhalt der QuelleTumor shape and texture evolution may highlight internal modifications resulting from the progression of cancer. In this work, we want to study the contribution of delta-radiomics features to cancer-evolution prediction. Our goal is to provide a complete pipeline from the 3D reconstruction of the volume of interest to the prediction of its evolution, using routinely acquired data only.To this end, we first analyse a subset of MRI(-extracted) radiomics biomarquers in order to determine conditions that ensure their robustness. Then, we determine the prerequisites of features reliability and explore the impact of both reconstruction and image processing (rescaling, grey-level normalization). A first clinical study emphasizes some statistically-relevant MRI radiomics features associated with event-free survival in anal carcinoma.We then develop machine-learning models to improve our results.Radiomics and machine learning approaches were then combined in a study on high grade soft tissu sarcoma (STS). Combining Radiomics and machine-learning approaches in a study on high-grade soft tissue sarcoma, we find out that a T2-MRI delta-radiomic signature with only three features is enough to construct a classifier able to predict the STS histological response to neoadjuvant chemotherapy. Our ML pipeline is then trained and tested on a middle-size clinical dataset in order to predict early metastatic relapse of patients with breast cancer. This classification model is then compared to the relapsing time predicted by the mechanistic model.Finally we discuss the contribution of deep-learning techniques to extend our pipeline with tumor automatic segmentation or edema detection
Ahrari, Shamimeh. „Implémentation de la radiomique en routine clinique : approche individuelle et analyse de la composante temporelle par des approches d’apprentissage automatique en TEP pour la neuro-oncologie“. Electronic Thesis or Diss., Université de Lorraine, 2024. https://docnum.univ-lorraine.fr/public/DDOC_T_2024_0092_AHRARI.pdf.
Der volle Inhalt der QuelleWith the growing emphasis on personalized medicine, a non-invasive glioma characterization tool is essential, aiding clinicians in making optimal decisions to improve patient survival while preserving their quality of life. Medical imaging techniques such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offer a promising solution in neuro-oncology for the non-invasive diagnosis and monitoring of gliomas. In this context, PET molecular imaging, particularly with amino acid radiotracers such as 18F-FDOPA, is currently recommended by international guidelines as an adjunct to conventional MRI. Advancements in image processing are now focused on quantifying tumor heterogeneity through the massive extraction of characteristics, known as radiomics analysis. However, this analysis has primarily been applied to static images acquired at a fixed time, ignoring the temporal dimension. In contrast, dynamic analysis offers a unique perspective by capturing the temporal variations of tumor metabolism, providing complementary information to static analysis. While region-based dynamic parameters have shown promising results for the initial diagnosis, they have limitations in detecting glioma recurrences. This thesis therefore explores the potential of machine learning-based radiomics analysis on dynamic PET acquisition at the voxel level to identify biomarkers of interest for glioma cancer indications. The temporal dimension of radiomics analysis can be addressed on two levels: by tracking the kinetics of tumor metabolism through single-time-point dynamic acquisition, and by monitoring changes in patient status over multiple examinations. Initially, this work investigated the impact of point spread function deconvolution, a common post-reconstruction technique at our institution, on voxel-based dynamic analysis. Subsequently, the first aspect of the temporal dimension was evaluated through radiomics analysis of single-time-point dynamic PET images at the voxel level. The prognostic value of this analysis for glioma recurrence detection was modest. Therefore, the temporal dimension of radiomics analysis was further explored by examining changes in radiomics features between two consecutive PET scans, aiming to monitor the post-treatment status of patients with glioma. A multicenter validation study was then conducted to assess the potential of integrating radiomics analysis into clinical practice. The objective was to investigate the impact of an explainable radiomics model on the diagnostic performance of physicians in determining the aggressiveness of suspected gliomas at the initial diagnosis. To go further, the feasibility of adapting deep learning algorithms to the analysis of 18F-FDOPA PET imaging is encouraging. This approach could provide greater flexibility in model explainability while capturing the complex relationships between PET imaging features and patient outcomes
Pattiam, Giriprakash Pavithran. „Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography“. Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398.
Der volle Inhalt der QuelleKhalid, Fahad. „Magnetic Resonance Imaging and Genomic Mutation in Diffuse Intrinsic Pontine Glioma : Machine Learning Approaches for a Comprehensive Analysis“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST006.
Der volle Inhalt der QuelleThe diagnosis of diffuse intrinsic pontine glioma (DIPG) in children stands as one of the most harrowing within pediatric oncology. Despite numerous clinical trials exploring various treatments, the prognosis remains bleak, with most patients succumbing between 9 to 11 months post-diagnosis. Key gene mutations linked to DIPG include H3K27M, ACVR1, and TP53. Each mutation has distinct characteristics, leading physicians to suggest tailored therapies, underscoring the importance of accurate mutation detection in guiding treatment. Located in the crucial region of the brainstem, the pons, DIPG tumors pose significant biopsy risks due to potential neurological damage. Hence, MRI could become a primordial diagnostic tool for these tumors, assessing their spread and gauging therapy responses. Its use to predict accurate gene mutation, and identify long-term survivors, could enhance patient care significantly. Within this framework, radiomics transforms images into vast data sources, extracting features like shape and texture to aid decision-making. The objective of this thesis is to refine mutation prediction and pinpoint long-term survivors, emphasizing image normalization and the applicability of radiomic models. Our study utilized a retrospective database from Gustave Roussy Institute, encompassing 80 patients MRI data and their respective clinical data. These MRI images highlighted issues in radiomic studies, such as bias field inhomogeneity and the "scanner effect". To address these challenges, a dedicated MR image normalization pipeline was implemented, and radiomic features underwent ComBat harmonization. Given the dataset's missing modalities, a multi-model strategy was employed, leading to 16 distinct models based on various radiomic and clinical feature combinations. This approach was then streamlined into a multi-modal method, reducing the number of models to five. The results from the ensemble of these models proved to be the most promising. This multi-modal strategy incorporated a feature selection phase, pinpointing the most pertinent features. Additionally, this method was applied to identify long-term survivors and was complemented by the ICARE framework for a nuanced survival analysis output
Biondi, Michelangelo. „A general method for radiomic features selection - A SPECT simulation study“. Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1086938.
Der volle Inhalt der QuelleShafiq, ul Hassan Muhammad. „Characterization of Computed Tomography Radiomic Features using Texture Phantoms“. Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7642.
Der volle Inhalt der QuelleLeger, Stefan. „Radiomics risk modelling using machine learning algorithms for personalised radiation oncology“. 2018. https://tud.qucosa.de/id/qucosa%3A34254.
Der volle Inhalt der QuelleAltini, Nicola. „Computational imaging for precision medicine: the emergence of radiomics, pathomics and deep learning“. Doctoral thesis, 2022. https://hdl.handle.net/11589/245880.
Der volle Inhalt der Quelle„Texture Analysis Platform for Imaging Biomarker Research“. Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.46331.
Der volle Inhalt der QuelleDissertation/Thesis
Doctoral Dissertation Biomedical Informatics 2017