Machine Learning for Medical Image Analysis
Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children
For more details, read the article here.
This package contains deep learning tools for instance-level classification of congenital abnormalities of the kidney and urinary tract (CAKUT), as described in the following papers:
- Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Katherine Fischer, Susan L Furth, Yong Fan, Gregory E Tasian. Multi-instance Deep Learning of Ultrasound Imaging Data for Pattern Classification of Congenital Abnormalities of the Kidney and Urinary Tract in Children. Urology. 2020 Aug;142:183-189. doi: 10.1016/j.urology.2020.05.019. Epub 2020 May 20. PMID: 32445770; PMCID: PMC7387180.
- Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Katherine Fischer, Susan L Furth, Gregory E Tasian, Yong Fan. Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1347-1350. doi: 10.1109/isbi45749.2020.9098506. Epub 2020 May 22. PMID: 33850604; PMCID: PMC8040672.
Classification Of Morphological Patterns using Adaptive Regional Elements (COMPARE)
NITRC: CBICA: COMPARE: Tool/Resource Info
COMPARE is a method for classification of structural brain magnetic resonance (MR) images, which is a combination of deformation-based morphometry and machine learning methods. Before running classification, a morphological representation of the anatomy of interest is obtained from structural MR brain images using a high-dimensional mass-preserving template warping method [1, 2]. Regions that display strong correlations between tissue volumes and classification (clinical) variables learned from training samples are extracted using a watershed segmentation algorithm. To achieve robustness to outliers, the regional smoothness of the correlation map is estimated by a cross-validation strategy. A volume increment algorithm is then applied to these regions to extract regional volumetric features. To improve efficiency and generalization ability of the classification, a feature selection technique using Support Vector Machine-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. Although the algorithm is designed for structural brain image classification, it is readily applicable for functional brain image classification with proper feature images.
For more details visit COMPARE.
- COMPARE: Classification Of Morphological Patterns using Adaptive Regional Elements Yong Fan, Dinggang Shen, Ruben C. Gur, Raquel E. Gur, Christos Davatzikos IEEE Transactions on Medical Imaging, 93-105, Vol. 26, No. 1, 2007