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unsupervised_learning

Unsupervised learning

For unsupervised learning, no prelabeling is required. These algorithms cluster data points based on similarities in features and can be powerful tools for detecting previously unknown patters in multidimensional data 1).

The progress in this field of applied machine learning (ML) is continuously driven by the growing amount of available data and the increasing computational power 2) 3).


To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidus component identification and quantification using unsupervised machine learning algorithm, and to evaluate the association between intervening nidal brain parenchyma and radiation-induced changes (RICs) after stereotactic radiosurgery (SRS).

Fully automated segmentation via unsupervised classification with fuzzy c-means clustering was used to analyze AVM nidus on T2-weighted magnetic resonance imaging. The proportions of vasculature, brain parenchyma, and cerebrospinal fluid (CSF) were quantified. This was compared to manual segmentation. Association between brain parenchyma component and RIC development was assessed.

The proposed algorithm was applied to 39 unruptured AVMs. This included 17 female and 22 male patients with a median age of 27 years. The median percentages of the constituents were as follows: vasculature (31.3%), brain parenchyma (48.4%), and CSF (16.8%). RICs were identified in 17 (43.6%) of 39 patients. Compared to manual segmentation, the automated algorithm was able to achieve a Dice similarity index of 79.5% (sensitivity=73.5% and specificity=85.5%). RICs were associated with higher proportions of intervening nidal brain parenchyma (52.0% vs. 45.3%, p=0.015). Obliteration was not associated with a higher proportions of nidal vasculature (36.0% vs. 31.2%, p=0.152).

The automated segmentation algorithm was able to achieve classification of AVM nidus components with relative accuracy. Higher proportions of intervening nidal brain parenchyma were associated with RICs 4).

1)
Deo RC. Machine learning in medicine. Circulation . 2015;132(20):1920-1930.
2)
Rodrigues JFJr, Paulovich FV, de Oliveira MC, de Oliveira ONJr. On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis. Nanomedicine (Lond) . 2016;11(8):959-982.
3)
Coveney PV, Dougherty ER, Highfield RR. Big data need big theory too. Philos Trans A Math Phys Eng Sci . 2016;374(2080).
4)
Lee CC, Yang HC, Lin CJ, Chen CJ, Wu HM, Shiau CY, Guo WY, Hung-Chi Pan D, Liu KD, Chung WY, Peng SJ. Intervening nidal brain parenchyma and risk of radiation-induced changes after radiosurgery for brain arteriovenous malformation: a study using unsupervised machine learning algorithm. World Neurosurg. 2019 Jan 21. pii: S1878-8750(19)30103-2. doi: 10.1016/j.wneu.2018.12.220. [Epub ahead of print] PubMed PMID: 30677586.
unsupervised_learning.txt · Last modified: 2019/02/14 01:00 (external edit)