基于因果驱动的一次性学习用于前列腺MRI癌症分级
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI
AI总结：
本文提出一种因果驱动的一次性学习方法，通过提取特征图间因果关系并结合元学习流程，在低数据场景下提升前列腺MRI癌症分级的可靠性与可解释性。
摘要：
在本文中，我们提出了一种自动分类医学图像的新方法，该方法学习并利用图像中的弱因果信号。我们的框架由一个卷积神经网络骨干和一个因果提取器模块组成，该模块提取特征图之间的因果关系，从而能够在图像某一位置出现某个特征时，为模型提供图像其他位置存在另一特征的信息。为了评估我们的方法在低数据场景下的有效性，我们在一次性学习（One-shot learning）方案中训练该因果驱动架构，并提出了一种新的元学习流程，包括使用相关类别但不同粒度级别设计的元训练和元测试任务。我们在一个公开的前列腺MRI图像数据集上进行了二分类和多分类实验。为了验证所提出的因果驱动模块的有效性，我们进行了消融研究，并使用类激活图进行定性评估，以突出强烈影响网络决策过程的区域。我们的研究结果表明，特征之间的因果关系在增强模型识别相关信息的能力以及产生更可靠、更可解释的预测方面发挥着关键作用。这将使其成为医学图像分类任务中一种有前景的方法。
英文摘要：
In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme, where we propose a new meta-learning procedure entailing meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network's decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
