Machine learning approaches in medical image analysis: From detection to diagnosis

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

[1]  Bram van Ginneken,et al.  A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays , 2015, IEEE Transactions on Medical Imaging.

[2]  Georg Langs,et al.  Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks , 2015, IPMI.

[3]  Wiro J Niessen,et al.  Hippocampal shape is predictive for the development of dementia in a normal, elderly population , 2014, Human brain mapping.

[4]  Marco Loog,et al.  Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Arna van Engelen,et al.  Multi-Center MRI Carotid Plaque Component Segmentation Using Feature Normalization and Transfer Learning , 2015, IEEE Transactions on Medical Imaging.

[6]  Wiro J Niessen,et al.  Three-Dimensional Carotid Ultrasound Plaque Texture Predicts Vascular Events , 2014, Stroke.

[7]  Marleen de Bruijne,et al.  Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols , 2015, IEEE Trans. Medical Imaging.

[8]  Lauge Sørensen,et al.  Texture-Based Analysis of COPD: A Data-Driven Approach , 2012, IEEE Transactions on Medical Imaging.

[9]  M. Giger,et al.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. , 2008, Medical physics.

[10]  Peter Mountney,et al.  Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data , 2014, Medical Image Anal..

[11]  Marleen de Bruijne,et al.  Weighting training images by maximizing distribution similarity for supervised segmentation across scanners , 2015, Medical Image Anal..