Abstract：
Brain tumors are a threat to life for every other human being, be it adults or children. Gliomas are one of the deadliest brain tumors with an extremely difficult diagnosis. The reason is their complex and heterogenous structure which gives rise to subjective as well as objective errors. Their manual segmentation is a laborious task due to their complex structure and irregular appearance. To cater to all these issues, a lot of research has been done and is going on to develop AI-based solutions that can help doctors and radiologists in the effective diagnosis of gliomas with the least subjective and objective errors, but an end-to-end system is still missing. An all-in-one framework has been proposed in this research. The developed end-to-end multi-task learning (MTL) architecture with a feature attention module can classify, segment, and predict the overall survival of gliomas by leveraging task relationships between similar tasks. Uncertainty estimation has also been incorporated into the framework to enhance the confidence level of healthcare practitioners. Extensive experimentation was performed by using combinations of MRI sequences. Brain tumor segmentation (BraTS) challenge datasets of 2019 and 2020 were used for experimental purposes. Results of the best model with four sequences show 95.1% accuracy for classification, 86.3% dice score for segmentation, and a mean absolute error (MAE) of 456.59 for survival prediction on the test data. It is evident from the results that deep learning-based MTL models have the potential to automate the whole brain tumor analysis process and give efficient results with least inference time without human intervention. Uncertainty quantification confirms the idea that more data can improve the generalization ability and in turn can produce more accurate results with less uncertainty. The proposed model has the potential to be utilized in a clinical setup for the initial screening of glioma patients.
摘要：
脑肿瘤是对其他人类生命的威胁，无论是成人还是儿童。胶质瘤是最致命的脑肿瘤之一，诊断极其困难。原因是它们的复杂和异质结构，导致主观和客观错误。由于其复杂的结构和不规则的外观，它们的手动分割是一项艰巨的任务。为了解决所有这些问题，已经做了很多研究，并正在开发基于AI的解决方案，可以帮助医生和放射科医生以最少的主观和客观错误有效诊断胶质瘤，但是仍然缺少端到端系统。本研究提出了一个一体化框架。开发的端到端多任务学习（MTL）架构，具有特征注意模块，可以分类，段,并通过利用相似任务之间的任务关系来预测胶质瘤的总体生存率。不确定性估计也已被纳入框架，以提高医疗保健从业人员的信心水平。通过使用MRI序列的组合进行广泛的实验。2019年和2020年的脑肿瘤分割（BraTS）挑战数据集用于实验目的。具有四个序列的最佳模型的结果显示分类准确率为95.1％，分割的骰子得分为86.3%，对测试数据进行生存预测的平均绝对误差(MAE)为456.59。从结果可以明显看出，基于深度学习的MTL模型有可能自动化整个脑肿瘤分析过程，并在没有人工干预的情况下以最少的推理时间给出有效的结果。不确定性量化证实了这样一种观点，即更多的数据可以提高泛化能力，进而可以用更少的不确定性产生更准确的结果。所提出的模型具有在临床设置中用于神经胶质瘤患者的初始筛查的潜力。
