Cluster Activation Mapping with Applications to Medical Imaging
An open question in deep clustering is how to understand what in the image is creating the cluster assignments. This visual understanding is essential to be able to trust the results of an inherently complex algorithm like deep learning, especially when the derived cluster assignments may be used to inform decision-making or create new disease sub-types. In this work, we developed novel methodology to generate CLuster Activation Mapping (CLAM) which combines an unsupervised deep clustering framework with a modification of Score-CAM, an approach for discriminative localization in the supervised setting. We evaluated our approach using a simulation study based on computed tomography scans of the lung, and applied it to 3D CT scans from a sarcoidosis population to identify new clusters of sarcoidosis based purely on CT scan presentation.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDecision MakingDeep ClusteringOpen-Ended Question AnsweringSimilar Papers 제목 키워드 기반
Understanding Deep Neural Network Predictions for Medical Imaging Applications
Computer-aided detection has been a research area attracting great interest in the past decade. Machine learning algorithms have been utilized extensively for this application as they provide a valuable second opinion to…
BIG-bench Machine LearningDecision MakingA Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation
Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment. To make the labeling process less laborious and dependent on e…
Brain Tumor SegmentationObject LocalizationSegmentationSemantic Segmentation+3MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Automated diagnosis with artificial intelligence has emerged as a promising area in the realm of medical imaging, while the interpretability of the introduced deep neural networks still remains an urgent concern. Althoug…
Brain Tumor ClassificationBrain Tumor SegmentationTumor SegmentationUnsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models in medical imaging applications. This work is motivated by the observation that class activation maps (CAMs) for in-di…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionDiagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning
Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This si…
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)