... training instances/workers per CPU node to improve utilization 4. Use large batches and learning rate scaling to achieve fast convergence ... CNN for Large Microscopy Image Classification 465 2 Multi-scale Convolutional Neural Network.

... CNN or MIL pooling, and using self-supervised methods, SimCLR and SSMIL proposed by us. We show that using either patch or image level Contrastive Learning provides representation as good as training ... multiscale-CNN for large microscopy ...

To improve the performance of the current CNN model towards classification of scaled images, this work has performed investigations on different techniques: (i) exploration of (global) high-level, low-resolution CNN feature map augmentation ...

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining.

Presented is a deep learning based computational approach to solve the problem of enhancing the resolution of images gained from commonly available low magnification scanners, also known as the image super-resolution (SR) problem.

However, learning a robust deep CNN model for object recognition is still quite challenging because image classification and object detection is a severely unbalanced large-scale problem.

Deep neural network models are commonly used in various real-life applications due to their high prediction accuracy for different tasks.

Each section of the book is preceded by an introduction, which should help newcomers understand the subject.

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning ...

... Image Data” Section “Dropout-Enabled Ensemble Learning for Multi-Scale Biomedical Image Classification” Section “A Weighted Average-Based Classification Method” 0.80 0.75 of natural images. A rapidly growing collection of efforts are ...