Deep learning and medical diagnosis

Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective

E-learning in Medical Diagnosis

The massive amount of data, required to learn how to diagnose, can be organized and turned into information to physicians using computers and Internet. In addition to atlases, the collaboration among professors, researchers, and students are decisive issues to consolidate the diagnostic knowledge. In order to tackle this problem, we propose a collaborative learning environment to support group ...

Inductive and Bayesian learning in medical diagnosis

Although successful in medical diagnostic problems inductive learning systems were not widely accepted in medical practice In this paper two di erent approaches to machine learning in medical appli cations are compared the system for inductive learning of decision trees Assistant and the naive Bayesian classi er Both methodologies were tested in four medical diagnostic problems localization of ...

Deep Similarity Learning for Multimodal Medical Images

An effective similarity measure for multi-modal images is crucial for medical image fusion in many clinical applications. The underlining correlation across modalities is usually too complex to be modelled by intensity-based statistical metrics. Therefore, approaches of learning a similarity metric are proposed in recent years. In this work, we propose a novel deep similarity learning method th...

Deep Learning in Medical Imaging: General Overview.

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the co...