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Jaume Amores, David Geronimo, & Antonio Lopez. (2010). Multiple instance and active learning for weakly-supervised object-class segmentation. In 3rd IEEE International Conference on Machine Vision.
Abstract: In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set.
Keywords: Multiple Instance Learning; Active Learning; Object-class segmentation.
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Jaume Amores, N. Sebe, & Petia Radeva. (2005). Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors.
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Jaume Amores, N. Sebe, & Petia Radeva. (2005). Efficient Object-Class Recognition by Boosting Contextual Information.
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Jaume Amores, N. Sebe, & Petia Radeva. (2006). Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier. PRL - Pattern Recognition Letters, 27(3), 201–209.
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Jaume Amores, N. Sebe, & Petia Radeva. (2007). Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81).
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Jaume Amores, N. Sebe, & Petia Radeva. (2007). Class-Specific Binaryy Correlograms for Object Recognition. In British Machine Vision Conference.
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Jaume Amores, N. Sebe, Petia Radeva, Theo Gevers, & A. Smeulders. (2004). Boosting Contextual Information in Content-based Image Retrieval.
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Jaume Amores, & Petia Radeva. (2003). Non-rigid Registration of Vessel Structures in IVUS Images.
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Jaume Amores, & Petia Radeva. (2003). Elastic Matching and Retrieval of IVUS Images Using Contextual Information.
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Jaume Amores, & Petia Radeva. (2004). Registration and retrieval of medical images. Application to IVUS.
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Jaume Amores, & Petia Radeva. (2005). Registration and Retrieval of Highly Elastic Bodies using Contextual Information. PRL - Pattern Recognition Letters, 26(11), 1720–1731.
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Jaume Amores, & Petia Radeva. (2005). Retrieval of IVUS Images Using Contextual Information and Elastic Matching. International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657).
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Jaume Garcia. (2002). Propagacio de fronts per a la segmentacio en imatges IVUS.
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Jaume Garcia. (2009). Statistical Models of the Architecture and Function of the Left Ventricle (Debora Gil, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Cardiovascular Diseases, specially those affecting the Left Ventricle (LV), are the leading cause of death in developed countries with approximately a 30% of all global deaths. In order to address this public health concern, physicians focus on diagnosis and therapy planning. On one hand, early and accurate detection of Regional Wall Motion Abnormalities (RWMA) significantly contributes to a quick diagnosis and prevents the patient to reach more severe stages. On the other hand, a thouroughly knowledge of the normal gross anatomy of the LV, as well as, the distribution of its muscular fibers is crucial for designing specific interventions and therapies (such as pacemaker implanction). Statistical models obtained from the analysis of different imaging modalities allow the computation of the normal ranges of variation within a given population. Normality models are a valuable tool for the definition of objective criterions quantifying the degree of (anomalous) deviation of the LV function and anatomy for a given subject. The creation of statistical models involve addressing three main issues: extraction of data from images, definition of a common domain for comparison of data across patients and designing appropriate statistical analysis schemes. In this PhD thesis we present generic image processing tools for the creation of statistical models of the LV anatomy and function. On one hand, we use differential geometry concepts to define a computational framework (the Normalized Parametric Domain, NPD) suitable for the comparison and fusion of several clinical scores obtained over the LV. On the other hand, we present a variational approach (the Harmonic Phase Flow, HPF) for the estimation of myocardial motion that provides dense and continuous vector fields without overestimating motion at injured areas. These tools are used for the creation of statistical models. Regarding anatomy, we obtain an atlas jointly modelling, both, LV gross anatomy and fiber architecture. Regarding function, we compute normality patterns of scores characterizing the (global and local) LV function and explore, for the first time, the configuration of local scores better suited for RWMA detection.
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Jaume Garcia. (2004). Generalized Active Shape Models Applied to Cardiac Function Analysis. Master's thesis, , .
Abstract: Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown.
Keywords: Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography.
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