AIMC Topic: Unsupervised Machine Learning

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An unsupervised learning method to identify reference intervals from a clinical database.

Journal of biomedical informatics
Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert...

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

IEEE transactions on bio-medical engineering
Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection,...

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training.

IEEE transactions on bio-medical engineering
Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to pre...

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems.

Scientific reports
Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitiv...

High Throughput Multispectral Image Processing with Applications in Food Science.

PloS one
Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image proce...

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

IEEE/ACM transactions on computational biology and bioinformatics
In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words usin...

Dictionary-Driven Ischemia Detection From Cardiac Phase-Resolved Myocardial BOLD MRI at Rest.

IEEE transactions on medical imaging
Cardiac Phase-resolved Blood-Oxygen-Level Dependent (CP-BOLD) MRI provides a unique opportunity to image an ongoing ischemia at rest. However, it requires post-processing to evaluate the extent of ischemia. To address this, here we propose an unsuper...

Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ.

The Journal of comparative neurology
Generation of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology f...

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

Medical image analysis
This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more ac...

Extracting latent brain states--Towards true labels in cognitive neuroscience experiments.

NeuroImage
Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the thresh...