What is the role of feature maps in CNNs used for medical image analysis (e.g., disease detection, segmentation)?

Feb 12, 2021 ... We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Jan 1, 2023 ... keywords = "CNN, Feature map Augmentation, Global Features, Scale-Invariant, Vision System, Convolutional Neural Network, Feature Map ... Our method does not require fine-tuning the network weights and halves the data transfer volumes to the main memory by compressing feature maps, which are ... A local feature CNN extracts the coarse-level feature maps F ~ A and F ~ ... feature map. Coarse matches will be refined within this local window to a sub ... We present a method for boosting the performance of the Convolutional Neural Network (CNN) by reducing the covariance between the feature maps of the ... May 26, 2023 ... We propose augmenting intermediate feature maps of CNNs. Specifically, we pass them through a novel Augmentation Layer to prevent models from overfitting. Jul 13, 2021 ... ... feature map. However, in CNN, pooling is applied to feature maps that result from each filter. Flattening. You can think of CNN as a sequence ... In this paper, we design a light CNN architecture that addresses these challenges. Specifically, we implemented Max-feature-map (MFM) into each convolutional ... Feb 16, 2022 ... Visualizing Feature Maps! In this tutorial, we apply various models trained on the Imagenet dataset which has 1000 categories/labels for images. Jan 29, 2017 ... Instead of using max-pooling layers to reduce the size of the feature maps, we use convolution layers with increased strides.