Novel Deep Learning for Detecting Cancer cells with Raman Spectroscopy

1. 拉曼光谱在化学中广泛应用于提供分子结构指纹，通过该指纹可以识别分子。细胞的拉曼光谱，特别是光谱的特征峰，包含了细胞的基本生化信息。因此，本课题的动机是我们希望将拉曼光谱应用于区分癌细胞和其他类型细胞的问题。通过对细胞进行拉曼映射，我们可以在该细胞中预先指定的矩形区域内均匀地测量不同位置的拉曼光谱。因此，对于每个细胞，我们都有一个单独的数据集，其中包含不同位置的多个拉曼光谱。对于单个拉曼光谱，强度是在拉曼位移波数（例如1000波数）的大范围内测量的。对于博士阶段的项目，我渴望对不同癌症发展阶段的癌细胞进行分类，即研究癌细胞的侵袭性。本课题的全部动机是：开发可扩展的机器学习算法和对不同癌症阶段的癌细胞进行分类。项目进展中任何潜在的问题都可以设置为额外的主题，例如用于医学图像分析的变分自编码器。潜在前景为该项目开发的方法可以进一步转移到其他研究领域。例如，图像分析、时间序列分析、人脸自适应识别、脑MRI分析等。该项目的新颖内容包括基于现代先进的深度学习算法开发可扩展的医学成像机器学习算法。项目总体框架包括数据预处理、降维、细胞拉曼光谱学习和细胞图像学习。除分类技术外，还应考虑数据预处理程序、平滑方法和降维技术。数据预处理过程可能与生化知识相结合。例如，分析前应仔细去除荧光、水、玻璃和环境的影响。其中一个例子是修正多项式拟合，它已被广泛用于从拉曼光谱中减去自荧光。它是一种通过重新分配拟合值来修改基于最小二乘多项式拟合的方法。在信号处理和降维方面，可以采用小波分析，也可以采用低通滤波器。合适的降维方法，如广泛使用的线性降维方法和其他非线性降维方法（如使用变分自编码器的流形学习，拉普拉斯特征映射）会对结果产生很大影响。可以考虑潜在的机器学习方法，例如boost（作为元学习算法）和高斯过程，它们可以用作深度学习技术进一步分析的基准。我将从深度前馈网络（简称mlp）开始研究。在初步了解mlp可以执行的程度之后，我将开始开发用于癌细胞检测的深度学习框架。例如，将适当地开发变分自编码器，以从训练数据中学习流形，从而生成低维表示，这可以提高分类任务的性能。对于拉曼光谱，可以使用卷积神经网络（简称cnn）和生成对抗网络（简称gan），因为细胞内的强度是在大范围波数的不同位置测量的。这一特点使cnn成为分析细胞拉曼数据的合适人选。这部分项目的潜在主题包括网络架构的设计、损失函数、注意机制。

1. A brief description of the topicRaman Spectroscopy is widely used in chemistry to provide a structural fingerprint by which molecules can be identified. Raman spectra of cells, especially the characteristic peaks of spectra, contain essential bio-chemical information of these cells. Therefore, the motivation of this topic is that we want to apply Raman Spectroscopy to the problem of distinguishing cancer cells from other types of cells. By using Raman Mapping for a cell, we can measure Raman spectra at different positions evenly within a pre-specified rectangle area in this cell. Therefore, for each cell, we have an individual dataset with multiple Raman spectra at different positions. For a single Raman spectrum, intensity is measured at a large range of Raman shift wavenumbers (e.g. 1000 wavenumbers).For a project at PhD level, I am eager to classify cancer cells from different cancer development stages, i.e. studying how aggressive cancer cells are. The whole motivations for this topic are: developing scalable machine learning algorithms and classifying cancer cells from different cancer stages. Any potential problems within the progress of the project can be set as extra topics, e.g. Variational Auto-encoder for medical image analysis.2. Potential prospectsThe methods developed for the project can be further transferred to other areas of research. For instance, image analysis, time series analysis, adaptive identification of human faces, and brain MRI analysis. The novel contents of the project include developing scalable machine learning algorithms for medical imaging which will be based on modern advanced deep learning algorithms.3. Outline of the studyThe whole framework of the project includes data pre-processing, dimension reduction, learning on Raman spectra of cells and learning on the images of cells.Data pre-processing procedures, smoothing methods and dimension reduction techniques should be taken into account as well as classification techniques. Data pre-processing procedures may incorporate with bio-chemical knowledge. For example, the effects of fluorescence, water, glass and environment should be carefully removed before analysis. One example is modified polynomial fitting, which has been widely used to subtract the auto-fluorescence from Raman spectra. It is a method that modifies the least-square-based polynomial fitting by reassignment of fitted values. In terms of signal processing and dimension reduction, wavelet analysis can be employed as well as low-pass filters. Suitable dimension reduction methods such as widely-used linear dimension reduction methods and other non-linear dimension reduction methods (e.g. manifold learning using Variational auto-encoder, Laplacian Eigenmaps) can have great impact on the results. Potential machine learning methods can be taken into account, e.g. Boosting (as a meta-learning algorithm) and Gaussian Processes, which can be used as benchmarks for further analyses with deep learning techniques.I will start the research with Deep Feedforward Networks (or MLPs, for short). After having some initial idea of to what extent MLPs can perform, I will start developing a deep learning framework for cancer cells detection. For instance, variational auto-encoders will be developed appropriately to learn manifolds from the training data and thus generate lower-dimensional representations, which can improve performance in classification tasks. For Raman Spectroscopy, Convolutional Neural Networks (or CNNs, for short) and Generative Adversarial Networks (or GANs, for short) can be used since intensities within a cell are measured at different positions across a large range of wave-numbers. This characteristic makes CNNs proper candidates to analyze Raman data of cells. The potential topics within this part of the project include the design of the architecture of networks, loss functions, attention mechanisms.