Improving minority classes' prediction accuracy using the geometric SMOTE algorithm

Douzas, G., Bacao, F., Fonseca, J., & Khudinyan, M. (2019). Imbalanced learning in land cover classification: Improving minority classes' prediction accuracy using the geometric SMOTE algorithm. Remote Sensing, 11(24), [3040]. https://doi.org/10.3390/rs11243040The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.publishersversionpublishe

Doctor of Philosophy

dissertationScene labeling is the problem of assigning an object label to each pixel of a given image. It is the primary step towards image understanding and unifies object recognition and image segmentation in a single framework. A perfect scene labeling framework detects and densely labels every region and every object that exists in an image. This task is of substantial importance in a wide range of applications in computer vision. Contextual information plays an important role in scene labeling frameworks. A contextual model utilizes the relationships among the objects in a scene to facilitate object detection and image segmentation. Using contextual information in an effective way is one of the main questions that should be answered in any scene labeling framework. In this dissertation, we develop two scene labeling frameworks that rely heavily on contextual information to improve the performance over state-of-the-art methods. The first model, called the multiclass multiscale contextual model (MCMS), uses contextual information from multiple objects and at different scales for learning discriminative models in a supervised setting. The MCMS model incorporates crossobject and interobject information into one probabilistic framework, and thus is able to capture geometrical relationships and dependencies among multiple objects in addition to local information from each single object present in an image. The second model, called the contextual hierarchical model (CHM), learns contextual information in a hierarchy for scene labeling. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. The CHM then incorporates the resulting multiresolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. We demonstrate the performance of CHM on different challenging tasks such as outdoor scene labeling and edge detection in natural images and membrane detection in electron microscopy images. We also introduce two novel classification methods. WNS-AdaBoost speeds up the training of AdaBoost by providing a compact representation of a training set. Disjunctive normal random forest (DNRF) is an ensemble method that is able to learn complex decision boundaries and achieves low generalization error by optimizing a single objective function for each weak classifier in the ensemble. Finally, a segmentation framework is introduced that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy images

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

[EN] Objective To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Methods Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. Results In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 +/- 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 +/- 0.054) and melanoma BM (eight features, AUC = 0.936 +/- 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 +/- 0.180). Conclusion Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels.This work has been partially funded by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R. Rafael Ortiz-Ramon was supported by grant ACIF/2015/078 from the Conselleria d'Educacio, Investigacio, Cultura i Esport of the Valencian Community (Spain). Andres Larroza was supported by grant FPU12/01140 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD).Ortiz-Ramón, R.; Larroza-Santacruz, A.; Ruiz-España, S.; Arana Fernandez De Moya, E.; Moratal, D. (2018). Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. European Radiology. 28(11):4514-4523. https://doi.org/10.1007/s00330-018-5463-6S451445232811Gavrilovic IT, Posner JB (2005) Brain metastases: epidemiology and pathophysiology. J Neurooncol 75:5–14Stelzer KJ (2013) Epidemiology and prognosis of brain metastases. Surg Neurol Int 4:S192–S202Soffietti R, Cornu P, Delattre JY et al (2006) EFNS Guidelines on diagnosis and treatment of brain metastases: report of an EFNS Task Force. Eur J Neurol 13:674–681Kaal ECA, Taphoorn MJB, Vecht CJ (2005) Symptomatic management and imaging of brain metastases. J Neurooncol 75:15–20Nayak L, Lee EQ, Wen PY (2012) Epidemiology of brain metastases. Curr Oncol Rep 14:48–54Bartelt S, Lutterbach J (2003) Brain metastases in patients with cancer of unknown primary. J Neurooncol 64:249–253Agazzi S, Pampallona S, Pica A et al (2004) The origin of brain metastases in patients with an undiagnosed primary tumor. Acta Neurochir (Wien) 146:153–157Pekmezci M, Perry A (2013) Neuropathology of brain metastases. Surg Neurol Int 4:245Zakaria R, Das K, Bhojak M et al (2014) The role of magnetic resonance imaging in the management of brain metastases: diagnosis to prognosis. Cancer Imaging 14:1–8Bekaert L, Emery E, Levallet G, Lechapt-Zalcman E (2017) Histopathologic diagnosis of brain metastases: current trends in management and future considerations. Brain Tumor Pathol 34:8–19Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446Yip SSF, Aerts HJWL (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31:809–816Mahmoud-Ghoneim D, Toussaint G, Constans JM, De Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987Fetit AE, Novak J, Peet AC, Arvanitis TN (2015) Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumors. NMR Biomed 28:1174–1184Zacharaki EI, Wang S, Chawla S et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618Georgiadis P, Cavouras D, Kalatzis I et al (2009) Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods. Magn Reson Imaging 27:120–130Larroza A, Moratal D, Paredes-Sánchez A et al (2015) Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. J Magn Reson Imaging 42:1362–1368Li Z, Mao Y, Li H et al (2016) Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR. Magn Reson Med 76:1410–1419Fink KR, Fink JR (2013) Imaging of brain metastases. Surg Neurol Int 4:S209–S219Larroza A, Bodí V, Moratal D (2016) Texture analysis in magnetic resonance imaging: review and considerations for future applications. In: Assessment of cellular and organ function and dysfunction using direct and derived MRI methodologies. InTech, Rijeka, Croatia, pp 75–106Leite M, Rittner L, Appenzeller S et al (2015) Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging. J Med Imaging 2:14002Mahmoud-Ghoneim D, Alkaabi MK, De Certaines JD, Goettsche F-M (2008) The impact of image dynamic range on texture classification of brain white matter. BMC Med Imaging 8:1–8Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Müller H (2014) Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal 18:176–196Ellingson BM, Bendszus M, Boxerman J et al (2015) Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 17:1188–1198Mayerhoefer ME, Breitenseher MJ, Kramer J et al (2005) Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging 22:674–680Waugh SA, Lerski RA, Bidaut L, Thompson AM (2011) The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. Med Phys 38:5058–5066Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266–277Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496Kuhn M, Johnson K (2013) Data pre-processing. In: Applied predictive modeling, 1st ed. Springer, New York, NY, pp 27–59Fernández-Delgado M, Cernadas E, Barro S et al (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th international conference on Machine learning - ICML ’08. ACM Press, Helsinki, Finland, pp 96–103Kuhn M, Johnson K (2013) Over-fitting and model tuning. In: Applied predictive modeling, 1st ed. Springer, New York, NY, pp 61–92Kuhn M, Johnson K (2013) An introduction to feature selection. In: Applied predictive modeling, 1st ed. Springer, New York, NY, pp 487–519Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A 99:6562–6566Provost F, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52:199–215Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach. In: 2017 I.E. 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, pp 1213–1216Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Seogwipo, pp 493–496Béresová M, Larroza A, Arana E, et al (2017) 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 1–10Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 38:89–101Chen W, Giger ML, Li H et al (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–57

Multiwavelength, Machine Learning, and Parallax Studies of X-ray Binaries in Three Local Group Galaxies

X-ray binary stars are rare systems consisting of a black hole or neutron star and a main-sequence companion star. They are useful probes of galaxy properties and interesting laboratories for extreme physical conditions. In this thesis, I investigated the X-ray binary population of three galaxies in the Local Group. The Sculptor Dwarf Spheroidal Galaxy offers the chance to study a primordial low-mass X-ray binary (LMXB) population in an isolated, low-metallicity environment. Combining X-ray, optical, and infrared observations, I have studied nine previously-identified and discovered four additional LMXB candidates in this galaxy. Of these candidates, all but one are either background galaxies or foreground stars, meaning that Sculptor is presently effectively devoid of bright LMXBs. If Sculptor is able to retain primordial LMXBs at a similar rate to globular clusters, it is likely that bright XRBs in globular clusters observed in the present day were dynamically formed. The Andromeda Galaxy has the largest catalogue of Chandra-studied X-ray sources of any nearby galaxy. I have used this population to test a proof-of-concept method for identifying X-ray binary candidates using machine learning algorithms trained on known sources. After testing a variety of commonly used algorithms, I find that the best-performing random forest algorithm can identify X-ray binary candidates with 85% accuracy. I have identified 16 new strong X-ray binary candidates and find that 4 sources classified as X-ray binaries by this method coincide with star clusters identified by the Panchromatic Hubble Andromeda Treasury project. The Milky Way\u27s X-ray binary population is the easiest to study but the most challenging for which to accurately measure distance. I have crossmatched Galactic X-ray binary catalogs to the second data release of the Gaia mission, finding candidate counterparts for 86 Galactic X-ray binaries. Distances to Gaia candidate counterparts are systematically smaller than those measured using Type I X-ray bursts, suggesting that these bursts do not consistently reach the Eddington limit. High-mass X-ray binaries are correlated with the Galaxy\u27s spiral arms and low-mass X-ray binaries are anti-correlated with the Galaxy\u27s spiral arms at a low level of significance