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，通过从例子中学习，而不是遵循特定的编程指令，执行对象和语音识别、生物识别、计算机视觉、自动化医疗诊断和数据挖掘等“智能任务”。随着大数据集在我们的社会中的激增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，因为它促进了对海量数据的高效搜索和挖掘，以及对数据的智能处理。建议的研究将集中于设计和分析新的机器学习算法，以应对通常难以大量获取的多维、非结构化和未注释数据(例如医疗数据)带来的挑战。新的和实质性的研究挑战出现在算法的分析和设计中，这些算法学习数据分布适应数据的非平稳性，并结合实际的内存限制、导致过度参数化的算法的复杂性、训练和计算速度的限制以及全局优化中的挑战。 研究目标分为两个主要主题：(1)计算的应用 学习理论和复杂性正则化在深层卷积多层分析中的应用 神经网络、深度随机森林分类器，以分析它们的收敛速度、收敛速度和学习速度。(2)主曲线和流形在医学成像中的应用，用于细胞学和组织病理学切片的稳健和准确的分割，白细胞和红细胞的计数和分类，以及超声和断层合成乳腺图像中的肿瘤的三维分割。利用深度卷积神经网络对医学图像进行自动特征提取，并与支持向量机、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名理科硕士。以及每年3名博士生，带着激动人心的研究挑战，让他们沉浸在当前机器学习的重要主题中。这项研究有望加深对深度网络学习的基本原理及其实用方面的理解，如快速、简单、可实现的具有良好性能的算法。

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.