Class activation mapping (CAM)
Citation, DOI, disclosures and article data
At the time the article was created Candace Makeda Moore had no recorded disclosures.
View Candace Makeda Moore's current disclosuresAt the time the article was last revised Andrew Murphy had no recorded disclosures.
View Andrew Murphy's current disclosures- CAM, GradCAM, GRADCAM, Grad-CAM
Class activation mapping is a method to generate heatmaps of images that show which areas were of high importance in terms of a neural networks for image classification. There are several variations on the method including Score-CAM and Grad-CAM (Gradient Weighted Class Activation Mapping). The heatmaps generated by CAM is a visualization that can be interpreted as telling us where in the image the neural net is (metaphorically) looking to make it's decision, however they do not tell us what particularities it might be looking at. In some cases the heatmaps from CAM can be used not only to inform what pixels were important in the neural network classification process of an image, but also for object localization.
References
- Kenneth A. Philbrick, Kotaro Yoshida, Dai Inoue, Zeynettin Akkus, Timothy L. Kline, Alexander D. Weston, Panagiotis Korfiatis, Naoki Takahashi, Bradley J. Erickson. What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images. (2018) American Journal of Roentgenology. 211 (6): 1184-1193. doi:10.2214/AJR.18.20331 - Pubmed
- Jared A. Dunnmon, Darvin Yi, Curtis P. Langlotz, Christopher Ré, Daniel L. Rubin, Matthew P. Lungren. Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. (2018) Radiology. 290 (2): 537-544. doi:10.1148/radiol.2018181422 - Pubmed
- F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers, D. Pfeiffer. Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization. (2019) Scientific Reports. 9 (1): 1. doi:10.1038/s41598-019-42557-4 - Pubmed
- Nelson Nunes, Bruno Martins, Nuno André da Silva, Francisca Leite, Mário J. Silva. A Multi-modal Deep Learning Method for Classifying Chest Radiology Exams. (2019) Digestive diseases and sciences. 63 (12): 323. doi:10.1007/978-3-030-30241-2_28 - Pubmed
Related articles: Artificial intelligence
- artificial intelligence (AI)
- imaging data sets
- computer-aided diagnosis (CAD)
- natural language processing
- machine learning (overview)
- visualizing and understanding neural networks
- common data preparation/preprocessing steps
- DICOM to bitmap conversion
- dimensionality reduction
- scaling
- centering
- normalization
- principal component analysis
- training, testing and validation datasets
- augmentation
- loss function
-
optimization algorithms
- ADAM
- momentum (Nesterov)
- stochastic gradient descent
- mini-batch gradient descent
-
regularisation
- linear and quadratic
- batch normalization
- ensembling
- rule-based expert systems
- glossary
- activation function
- anomaly detection
- automation bias
- backpropagation
- batch size
- computer vision
- concept drift
- cost function
- confusion matrix
- convolution
- cross validation
- curse of dimensionality
- dice similarity coefficient
- dimensionality reduction
- epoch
- explainable artificial intelligence/XAI
- feature extraction
- federated learning
- gradient descent
- ground truth
- hyperparameters
- image dataset normalization
- image registration
- imputation
- iteration
- jaccard index
- linear algebra
- noise reduction
- normalization
- R (Programming language)
- radiomics quality score (RQS)
- Python (Programming language)
- segmentation
- semi-supervised learning
- synthetic and augmented data
- overfitting
- underfitting
- transfer learning
Unable to process the form. Check for errors and try again.