Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions.
This function adds a weighted focal coefficient and combines two traditional loss functions.
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.
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.
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.
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.
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.