Dr. Daniel L. Rubin Profile

Dr. Daniel L. Rubin

Associate Professor of Biomedical Data Science at Stanford Univ

SPIE Involvement:

Author

Publications (7)

SPIE Journal Paper | 28 June 2023

Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients

Amara Tariq, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice Newsome, Daniel Rubin, Hari Trivedi, Judy Gichoya, Imon Banerjee

JMI, Vol. 10, Issue 03, 034004, (June 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.034004

KEYWORDS: Data modeling, Data fusion, Diseases and disorders, Image fusion, Chest imaging, Performance modeling, Modeling, Machine learning, COVID 19, Education and training

Read Abstract +

SPIE Journal Paper | 14 March 2020 Open Access

Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines

Sarah Mattonen, Dev Gude, Sebastian Echegaray, Shaimaa Bakr, Daniel Rubin, Sandy Napel

JMI, Vol. 7, Issue 04, 042803, (March 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.4.042803

KEYWORDS: Image segmentation, Feature extraction, Machine learning, Process modeling, Medical imaging, Image processing, Cancer, 3D image processing, Imaging systems, Image analysis

Read Abstract +

DOWNLOAD PAPER

Proceedings Article | 13 March 2019 Paper

A lung graph model for the classification of interstitial lung diseases on CT images

Guillaume Vanoost, Yashin Dicente Cid, Daniel Rubin, Adrien Depeursinge

Proceedings Volume 10950, 109503H (2019) https://doi.org/10.1117/12.2512866

KEYWORDS: Lung, Computed tomography, Image classification, Visualization, Tissues, Medicine

Read Abstract +

Diagnosing Interstitial Lung Diseases (ILD) is a difficult task. It requires experienced chest radiologists that may not be available in less-specialized health centers. Moreover, a correct diagnosis is needed to decide for an appropriate treatment and prognostic. In this paper, we focus on the classification of 3 common subtypes of ILDs: Usual Interstitial Pneumonia (UIP), Non-Specific Interstitial Pneumonia (NSIP) and Chronic Hypersensitivity Pneumonitis (CHP). We propose a graph model of the lungs built from a large dataset. The structure of the graph is inspired from medical knowledge of disease predominance, where the nodes correspond to 24 distinct regions obtained from lateral, anterior-posterior and vertical splits of the images. The adjacency matrix is built from distances between intensity distributions of distinct regions. Graphs models are interpretable and were successfully used in neuroimaging. However, to the best of our knowledge, this is the first attempt to use a graph model of the lungs for classifying ILDs. In the particular case of ILDs, graph methods are relevant for the following reasons. In order to differentiate between the subtypes, not only the types of local patterns of the disease are important but also their anatomical location. Therefore, we hypothesize that the comparison between regional distributions of Hounsfield Unit (HU) values is relevant to discriminate between the considered ILD subtypes. For instance, typical UIP shows a spatial predominance of reticular abnormalities and honeycombing in the peripheral regions of the lung bases. Therefore, we expect a marked difference of HU distributions between the central and peripheral regions of the lung bases. Moreover, the construction of the graph leads to an interpretable patient descriptor. The descriptor led to encouraging area under the Receiver Operating Characteristic (ROC) curve in 0.6-0.8 for one-versus-one classification configurations, which also showed to outperform feature sets based on a simple concatenation of regional HU distributions.

SPIE Journal Paper | 2 November 2017

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging

Imon Banerjee, Sadhika Malladi, Daniela Lee, Adrien Depeursinge, Melinda Telli, Jafi Lipson, Daniel Golden, Daniel Rubin

JMI, Vol. 5, Issue 01, 011008, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011008

KEYWORDS: Breast cancer, Performance modeling, Tumors, Magnetic resonance imaging, Data modeling, Wavelets, Statistical modeling, Image filtering, Statistical analysis, Breast

Read Abstract +

SPIE Journal Paper | 1 November 2017

Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions

Alfiia Galimzianova, Ziga Lesjak, Daniel Rubin, Boštjan Likar, Franjo Pernuš, Žiga Špiclin

JMI, Vol. 5, Issue 01, 011007, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011007

KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, Neuroimaging, Magnetism, Data modeling, Databases, Performance modeling, 3D modeling, Statistical analysis

Read Abstract +

SPIE Journal Paper | 3 April 2015

Content-based image retrieval in radiology: analysis of variability in human perception of similarity

Jessica Faruque, Christopher Beaulieu, Jarrett Rosenberg, Daniel Rubin, Dorcas Yao, Sandy Napel

JMI, Vol. 2, Issue 02, 025501, (April 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.2.025501

KEYWORDS: Radiology, Liver, Content based image retrieval, Computed tomography, Diagnostics, Standards development, Medical imaging, Visualization, Tumors, Data acquisition

Read Abstract +

Proceedings Article | 18 March 2013 Paper

Image patch-based method for automated classification and detection of focal liver lesions on CT

Mustafa Safdari, Raghav Pasari, Daniel Rubin, Hayit Greenspan

Proceedings Volume 8670, 86700Y (2013) https://doi.org/10.1117/12.2008624

KEYWORDS: Liver, Image classification, Visualization, Computed tomography, Associative arrays, Image segmentation, Principal component analysis, Image analysis, Algorithm development, Liver cancer

Read Abstract +

Showing 5 of 7 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years