Improving FTIR Spectroscopy-based Prostate Cancer Classification through Deep Learning.

项目目标：研究最适合在FTIR前列腺癌数据上执行各种机器学习任务的图像处理和神经网络方法。调查任何已开发的技术的可解释性，以提高可靠性，增加病理学家和临床医生的信任，并增加在临床环境中被采用的机会。项目方法：首先计划文献，特别是在前列腺癌诊断和化学领域，以及先前机器学习在FTIR数据中的应用。然后，对不同类型的深度神经网络和图像处理技术进行正在进行的实验，以找到进一步研究的方法将是重要的。不同的机器学习任务有不同的难度。与区分癌症和非癌症样本相比，对癌症进行分级一直是一项艰巨的任务。空间信息将通过使用但不限于马尔可夫随机场进行探索。与FTIR中的化学工程同事和病理学家合作将是重要的，有助于逐步更深入地了解问题，更好地理解数据及其意义，而处理数据是一项巨大的任务。

Project objectives:Investigate the image processing and neural network methods that work best to perform various machine learning tasks on FTIR prostate cancer data.Investigate the explainability of any developed techniques to aid reliability, increase trust from pathologists and clinicians, and increase chance of adoption in a clinical setting.Project approaches:First literature is planned, particularly in areas of prostate cancer diagnosis and chemistry, and previous applications of machine learning to FTIR data.Then ongoing experimentation with different types of deep neural networks and image processing techniques will be important to find methods to pursue further in research. Different machine learning tasks have different difficulties. Grading cancer will always be hard task compared to differentiating cancerous and non cancerous samples. Spatial information will be explored through the use of, but no limited to, the Markov random field.Collaborations with chemical engineering colleagues in FTIR and pathologists will be important and helpful for gradually gaining more insight to the problem and better understanding of the data and its meaning, while processing the data is a large undertaking.