arXiv 2408.020012024-08-04cs.CV

AdaCBM：一种用于可解释且准确诊断的自适应概念瓶颈模型

AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis

Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；Australian Catholic University（澳大利亚天主教大学）；Flinders University（弗林德斯大学）

Townim F. Chowdhury, Vu Minh Hieu Phan, Kewen Liao, Minh-Son To, Yutong Xie, Anton van den Hengel, Johan W. Verjans, Zhibin Liao

备注：: Accepted at MICCAI 2024, the 27th International Conference on Medical Image Computing and Computer Assisted Intervention

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AI总结：

针对CLIP与CBM用于医学诊断时的迁移学习及后处理微调失效问题，本文提出在CLIP和CBM之间插入自适应模块的AdaCBM，在保持概念可解释性的同时提升分类准确率。

摘要：

将CLIP等视觉-语言模型与概念瓶颈模型（CBM）相结合，为利用人类可理解的概念解释深度神经网络（DNN）决策提供了一种有前景的方法，可解决DNN的黑箱问题。尽管CLIP同时具备可解释性和零样本分类能力，但其在通用图像与文本数据上的预训练可能限制其分类准确率以及在医学图像诊断任务中的适用性，从而产生迁移学习问题。为保持可解释性并满足迁移学习需求，CBM方法通常在瓶颈模块之后设计后处理模块。然而，这种方式效果不佳。本文采取非传统思路，将CBM框架作为简单线性分类系统，从其几何表示的角度重新审视。分析揭示，CBM后的微调模块只是对系统的分类结果进行重新缩放和平移，未能充分利用系统的学习潜力。我们引入一个自适应模块，将其策略性地置于CLIP与CBM之间，以弥合源域与下游域之间的差距。这一简单而有效的方法在提升分类性能的同时，保留了该框架提供的可解释性。我们的工作提供了涵盖从概念发现到模型训练整个流程的综合解决方案，为利用GPT、CLIP和CBM的优势给出了一套整体性方案。

英文摘要：

The integration of vision-language models such as CLIP and Concept Bottleneck Models (CBMs) offers a promising approach to explaining deep neural network (DNN) decisions using concepts understandable by humans, addressing the black-box concern of DNNs. While CLIP provides both explainability and zero-shot classification capability, its pre-training on generic image and text data may limit its classification accuracy and applicability to medical image diagnostic tasks, creating a transfer learning problem. To maintain explainability and address transfer learning needs, CBM methods commonly design post-processing modules after the bottleneck module. However, this way has been ineffective. This paper takes an unconventional approach by re-examining the CBM framework through the lens of its geometrical representation as a simple linear classification system. The analysis uncovers that post-CBM fine-tuning modules merely rescale and shift the classification outcome of the system, failing to fully leverage the system's learning potential. We introduce an adaptive module strategically positioned between CLIP and CBM to bridge the gap between source and downstream domains. This simple yet effective approach enhances classification performance while preserving the explainability afforded by the framework. Our work offers a comprehensive solution that encompasses the entire process, from concept discovery to model training, providing a holistic recipe for leveraging the strengths of GPT, CLIP, and CBM.

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。