Visual Explanations: Activation-based Acute Lymphoblastic Leukemia Cell Classification

The saliency methods are widely used for generating heatmaps that emphasize the important portions of an input image for deep networks on a specific classification task. Interpretability is crucial for deploying deep neural networks in real-world applications. However, the heatmaps produced by current visual explainable methods may contain or visualize particulars differently. To analyze and compare the visualization of different methods, such as Gradient-based, Activation-based, Perturbation-based, and Region-based methods, we empirically evaluated them on the acute lymphoblastic leukemia (cancer cell) classification task using state-of-the-art convolutional neural networks. We also visualized the essential pathological features (salient parts) that are the reasons for the classification results on the classification of normal versus malignant cell (CNMC) dataset.