AI in Medicine Books

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Published October 26, 2025 | Version v1.0.0
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Mastering Grad-CAM with TensorFlow: From Theory to Medical Applications

Authors/Creators

  • 1. Independent Researcher

Description

This book is dedicated to providing a comprehensive understanding of Grad-CAM (Gradient-weighted Class Activation Mapping) and its applications in medical imaging.
In recent years, deep learning models—particularly Convolutional Neural Networks (CNNs)—have played a transformative role in computer vision. However, one of their major limitations remains their "black box" nature, which makes it difficult for humans to interpret how these models reach specific decisions.

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References

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444; Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. ICLR 2015; He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR 2016; Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019; Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012; Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88; Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological Physics and Technology, 10(3), 257–273; Chest X-ray Pneumonia Dataset: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia ; Brain MRI Tumor Dataset: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection ; Selvaraju, R. R., et al. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. ICCV 2017; Chattopadhyay, A., et al. (2018). Grad-CAM++: Generalized Gradient-based Visual Explanations for Deep Convolutional Networks. WACV 2018; Springenberg, J. T., et al. (2015). Striving for Simplicity: The All Convolutional Net. ICLR Workshop; Zeiler, M. D., & Fergus, R. (2014). Visualizing and Understanding Convolutional Networks. ECCV 2014; Chollet, F. (2018). Deep Learning with Python. Manning Publications; TensorFlow Documentation: https://www.tensorflow.org ; Keras Applications: https://keras.io/api/applications/ ; TensorFlow Datasets (TFDS): https://www.tensorflow.org/datasets ; Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60; Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press; OpenCV Documentation: https://docs.opencv.org ; NumPy Documentation: https://numpy.org/doc/ .