Deep Learning with Applications in Pattern Recognition and Image Analysis

机器学习的目标是设计算法和统计模型，执行“智能任务”，如对象和语音识别，生物识别，计算机视觉，自动医疗诊断和数据挖掘，通过从示例中学习，而不是遵循特定的编程指令。 随着大数据集在我们社会中的扩散和大数据革命的开始，机器学习已经成为当代信息技术的关键组成部分，因为它促进了对大量数据的有效搜索和挖掘，以及对数据的智能处理。拟议的研究将专注于设计和分析新颖的机器学习算法，以满足多维，非结构化和未注释的数据往往难以大量获取（例如，医学数据）。新的和实质性的研究挑战出现在算法的分析和设计中，学习数据分布适应数据非平稳性，并将内存的实际约束，算法的复杂性导致overparametrization，训练和计算速度的限制以及全局优化的挑战。 研究目标分为两大主题：（1）计算 学习理论和复杂度正则化分析深度卷积和多层 神经网络，深度随机森林分类器，以分析其收敛性，收敛速度和学习速度。设计具有超核的新的高效且准确的核分类器;（2）主曲线和流形在医学成像中的应用，用于细胞病理学和组织病理学载玻片的鲁棒且准确的分割，白色和红细胞的计数和分类，以及用于超声和断层合成乳腺图像中的肿瘤的3D分割。通过深度卷积神经网络从医学图像中自动提取特征，并将其与SVM和随机森林等强大的分类器相结合。 拟议研究的培训部分将提供2名硕士和3名博士学生每年都有刺激的研究挑战，让他们沉浸在机器学习的重要当前主题中。该研究有望加深对深度网络学习基本原理及其实践方面的理解，例如快速、易于实现且性能良好的算法。

The goal of machine learning is to design algorithms and statistical models that perform “intelligent tasks” such as object and speech recognition, biometrics, computer vision, automated medical diagnosis and data mining, by learning from examples rather than following specific programming instructions. With the proliferation of big data sets in our society and onset of the big data revolution, machine learning has become a key component in contemporary information technologies as it facilitates efficient search and mining of large volumes of data, as well as intelligent processing of the data. The proposed research will focus on designing and analyzing novel machine learning algorithms that meet the challenges posed by multidimensional, unstructured and not annotated data often difficult to acquire in large quantities (e.g., medical data).. New and substantial research challenges arise in the analysis and design of algorithms that learn data distributions adapt to data nonstationarity and incorporate practical constraints on memory, complexity of algorithms leading to overparametrization, limits on training and computational speed and challenges in global optimization. The research objectives are divided into two main themes: (1) Application of computational learning theory and complexity regularization to analysis of deep convolutional and multilayer neural networks, deep random forest classifiers in order to analyze their convergence, rate of convergence and learning speed. Design of new efficient and accurate kernel classifiers with superkernels; (2) Application of principal curves and manifolds in medical imaging for robust and accurate segmentation of cytological and histopathological slides, counting and classifying white and red blood cells and for 3D segmentation of tumors in ultrasound and in tomosynthesis breast images. Automatic feature extraction from medical images by the deep convolutional neural networks and combining them with powerful classifiers such as SVM and random forest. The training component of the proposed research will provide 2 M.Sc. and 3 Ph.D. students each year with stimulating research challenges and immerse them in important current topics in machine learning. The research is expected to provide a deeper understanding of the fundamental principles of learning in deep networks as well as its practical aspects, such as fast and easy implementable algorithms with good performance.