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

通过深度学习改进基于 FTIR 光谱的前列腺癌分类。

• 批准号: 2903768
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903768
• 关键词: Improving; FTIR; Spectroscopy; based; Prostate

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.

项目目标：调查最佳地在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。对任何有助于可靠性，病理学家和临床医生的信任的信任的解释能力进行评估，并增加了在临床环境中采用的机会，并在临床环境中提高了针对临床方法的应用程序：尤其是在以前的数据中，并且在癌症诊断方面进行了研究。进行不同类型的深神经网络和图像处理技术的持续实验对于寻找进一步研究的方法至关重要。不同的机器学习任务有不同的困难。与区分癌性和非癌性样本相比，分级癌症总是很艰巨的任务。将通过使用Markov随机领域来探索空间信息。与FTIR的化学工程同事的合并和病理学家的合并将非常重要，并且有助于逐渐了解问题及其对数据及其含义的了解，同时处理数据是一项很大的承诺。