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Cross-Modal Data Programming Enables Rapid Medical Machine Learning
2020
Patterns
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Our results suggest that modern weak supervision techniques such as data programming may enable more rapid development and deployment of clinically useful machine-learning models. ...
Data programming now underpins many deployed machine-learning systems in the technology industry, even for critical applications. ...
This research used data or services provided by STARR, ''STAnford medicine Research data Repository,'' a clinical data warehouse containing live Epic Clarity warehouse data from Stanford Health Care, the ...
doi:10.1016/j.patter.2020.100019
pmid:32776018
pmcid:PMC7413132
fatcat:3bejjnkgkne3dgmepsp6ic3ysi
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
[article]
2019
arXiv
pre-print
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We propose cross-modal data programming, which generalizes this intuitive strategy in a theoretically-grounded way that enables simpler, clinician-driven input, reduces required labeling time, and improves ...
Labeling training datasets has become a key barrier to building medical machine learning models. ...
Conclusion In this work, we have described a new paradigm of weakly supervising medical machine learning problems via cross-modal data programming. ...
arXiv:1903.11101v1
fatcat:5yxqy7mnqrddfowcl4642eyxmu
MMDF2018 Workshop Report
[article]
2018
arXiv
pre-print
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higher veracity real-time data from a variety of modalities is expanding. ...
This next stage of understanding and discovery (i.e., the development of generalized solutions) can only be achieved via a high level cross-disciplinary aggregation of learnings, and this workshop was ...
cross-disciplinary learning. ...
arXiv:1808.10721v1
fatcat:w4pmt5vqgvcn7nltkisitjpyam
Conference Overview and Papers Program
2020
IS&T International Symposium on Electronic Imaging Science and Technology
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It emphasizes the interplay between mathematical theory, physical models, and computational algorithms that enable effective current and future imaging systems. ...
, their combination may be seen as example of a new paradigm of rapid, comprehensive, and information-rich computational microscopy.This session will explore cross-cutting themes in several modalities ...
Additionally, computational methods have helped overcome many of the practical issues associated with these sensors as well as enabled new imaging modalities. ...
doi:10.2352/issn.2470-1173.2020.14.coimg-a14
fatcat:vy4ul3slzzhg7hzp2pzxsafizy
Application of machine learning in ophthalmic imaging modalities
2020
Eye and Vision
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Machine learning (ML) is an important branch in the field of AI. ...
In these tasks, AI can analyze digital data in a comprehensive, rapid and non-invasive manner. ...
Conversely, in unsupervised learning, a machine is provided input data that are not explicitly labeled; the machine is then permitted to identify structures and patterns from the set of objects, without ...
doi:10.1186/s40662-020-00183-6
pmid:32322599
pmcid:PMC7160952
fatcat:nwtxlxbwdfdljnupbodgl4v57m
PHOTONAI—A Python API for rapid machine learning model development
2021
PLoS ONE
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A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. ...
Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. ...
Discussion We introduced PHOTONAI, a high-level Python API for rapid machine learning model development. ...
doi:10.1371/journal.pone.0254062
pmid:34288935
fatcat:temwqhamh5bopi4rmdvn2xbwwm
Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis
2018
Frontiers in Physics
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Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. ...
First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as ...
Machine learning is a promising approach to deal with large-scale medical data [26] . ...
doi:10.3389/fphy.2018.00051
fatcat:3ikjf4gqwfao5dngsqepuhmetu
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
[article]
2021
arXiv
pre-print
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However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in different areas separately. ...
We present Pykale - a Python library for knowledge-aware machine learning on graphs, images, texts, and videos to enable and accelerate interdisciplinary research. ...
With rapid development and growing interests in machine learning, many researchers hope to solve real-world interdisciplinary problems using machine learning. ...
arXiv:2106.09756v1
fatcat:mnuxoz26mbdexn5cobnpqhkmyi
Advances in Imaging Technology of Anterior Segment of the Eye
2021
Journal of Ophthalmology
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They enable accurate and precise assessment of structural and biomechanical alterations associated with anterior segment disorders. ...
This review will focus on these 4 new techniques, and a brief overview of these modalities will be introduced. ...
Machine learning algorithms including support vector machines or random forest models are programmed to adapt according to the input data and produce assumptions, e.g., determinations or predictions, based ...
doi:10.1155/2021/9539765
pmid:33688432
pmcid:PMC7925029
fatcat:hmu75rgpprcpne4sq3shxtfenm
Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation
2021
Current Directions in Biomedical Engineering
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First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. ...
Conclusions Developed Slicer-DeepSeg allows the development of novel AIassisted medical applications in neurosurgery. ...
The components of our developed system are described in the following subsections.
3D Slicer Core 3D Slicer is an open-source, cross-platform, and extensible software program for medical image computing ...
doi:10.1515/cdbme-2021-1007
fatcat:pbnytnvqpzcd7jojwdyyjr3uzu
Code-free deep learning for multi-modality medical image classification
2021
Nature Machine Intelligence
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In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification ...
The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. ...
The funder of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. ...
doi:10.1038/s42256-021-00305-2
fatcat:radpoz5vurgvfatkbpq4vuwibi
Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis
2021
Translational Psychiatry
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Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment ...
Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. ...
Two studies combined multiple modalities 40, 50 . As machine learning paradigm, 31% studies used support vector machine (SVM) for data-analysis, while 28% used logistic regression. ...
doi:10.1038/s41398-021-01286-x
pmid:33723229
fatcat:jtfbi6s3nzfjvndxesrf62c2xu
AI and Medical Imaging Informatics: Current Challenges and Future Directions
2020
IEEE journal of biomedical and health informatics
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More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context ...
of AI in big healthcare data analytics. ...
However, no single method has provided robust, cross-domain application solutions. The recent advent of machine learning approaches has provided good results in a wide range of applications. ...
doi:10.1109/jbhi.2020.2991043
pmid:32609615
pmcid:PMC8580417
fatcat:dcaefxwwqjfwla5asin34x2hxm
Application of AI and IoT in Clinical Medicine: Summary and Challenges
2021
Current Medical Science
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In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity ...
In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective ...
Machine Learning Machine learning is a special data-driven analysis method, which can build models automatically in order to find statistical patterns in high-dimensional and multivariate data sets. ...
doi:10.1007/s11596-021-2486-z
pmid:34939144
pmcid:PMC8693843
fatcat:3g3qpksktjhv5koqs3i7ylco7y
Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI
2022
Annals of Translational Medicine
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We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI, ...
It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability. ...
As most of the data sets had very small sample sizes, they could not be used as separate test sets, and as the k-fold cross validation provided an estimation of the expected estimation error, it enabled ...
doi:10.21037/atm-22-2961
fatcat:qrvvp4epvfcmnnlogw4s5oyypy
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