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Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré
2020 Patterns  
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]

Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew Lungren, Daniel Rubin, Christopher Ré
2019 arXiv   pre-print
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]

Chun-An Chou, Xiaoning Jin, Amy Mueller, Sarah Ostadabbas
2018 arXiv   pre-print
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  
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

Yan Tong, Wei Lu, Yue Yu, Yin Shen
2020 Eye and Vision  
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

Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz, Vincent Holstein, Jan Ernsting, Kelvin Sarink, Lukas Fisch, Jakob Steenweg, Leon Kleine-Vennekate, Julian Gebker, Daniel Emden, Dominik Grotegerd (+6 others)
2021 PLoS ONE  
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

Laszlo Papp, Clemens P. Spielvogel, Ivo Rausch, Marcus Hacker, Thomas Beyer
2018 Frontiers in Physics  
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]

Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs
2021 arXiv   pre-print
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

Sang Beom Han, Yu-Chi Liu, Karim Mohamed-Noriega, Jodhbir S. Mehta, Achim Langenbucher
2021 Journal of Ophthalmology  
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

Ramy A. Zeineldin, Pauline Weimann, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert
2021 Current Directions in Biomedical Engineering  
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

Edward Korot, Zeyu Guan, Daniel Ferraz, Siegfried K. Wagner, Gongyu Zhang, Xiaoxuan Liu, Livia Faes, Nikolas Pontikos, Samuel G. Finlayson, Hagar Khalid, Gabriella Moraes, Konstantinos Balaskas (+2 others)
2021 Nature Machine Intelligence  
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

Sem E. Cohen, Jasper B. Zantvoord, Babet N. Wezenberg, Claudi L. H. Bockting, Guido A. van Wingen
2021 Translational Psychiatry  
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

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
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

Zhao-xia Lu, Peng Qian, Dan Bi, Zhe-wei Ye, Xuan He, Yu-hong Zhao, Lei Su, Si-liang Li, Zheng-long Zhu
2021 Current Medical Science  
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

Xiaowen Chen, Mingyue Tang, Aimin Liu, Xiaoqin Wei
2022 Annals of Translational Medicine  
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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