Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification.
In this paper, we explore various techniques to explain the functional organization of brain tumor segmentation models and to extract visualizations of internal concepts to understand how these networks achieve highly accurate tumor segmentations.
This function adds a weighted focal coefficient and combines two traditional loss functions.
In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task.
However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc.
First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks.
Given estimates of p(y|x) from a predictive model, Saerens et al. (2002) proposed an efficient EM algorithm to correct for label shift that does not require model retraining.
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions.