Interpretability In Deep Learning
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Interpretability in Deep Learning
This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
Interpretability of Deep Learning Models
In recent years we have seen growth on interest for Deep Learning (DL) algorithms on a variety of problems, due to their outstanding performance. This is more palpable on a multitude of fields, where self-learning algorithms are becoming indispensable tools to help professionals solve complex problems. However as these models are getting better, they also tend to be more complex and are sometimes referred to as "Black Boxes". The lack of explanations for the resulting predictions and the inability of humans to understand those decisions seems problematic. In this project, different methods to increase the interpretability of Deep Neural Networks (DNN) such as Convolutional Neural Network (CNN) are studied. Additionally, how these interpretability methods or techniques can be implemented, evaluated and applied to real-world problems, by creating a python ToolBox.