Albert Montillo, Ph.D.
Assistant Professor

Albert Montillo is an Assistant Professor in the Lyda Hill Department of Bioinformatics with secondary appointments in the Department of Radiology, the Advanced Imaging Research Center, and Biomedical Engineering within the Graduate School of Biomedical Sciences.  He also directs the research of the Deep Learning for Precision Health lab.

Dr. Albert Montillo's formal training is through a Ph.D. in Computer Science with a focus on Medical Image Analysis from the University of Pennsylvania, Philadelphia, PA through training in departments of Computer and Information Science and Radiology. There his research focuses on medical image analysis in MRI including applications in neuroradiology and cardiology. Additional extensive training stems from his experience as a research scientist leading neuroimaging analysis efforts at the General Electric Research Center in New York and research at the Machine Intelligence and Perception Laboratory of Microsoft Research in Cambridge, United Kingdom, as well as machine learning based medical image analysis research at the Harvard-MIT Martinos Center for Biomedical Imaging, Boston, MA and Rutgers University in New Jersey.

Dr. Montillo's research focus is advancing the theory and application of deep learning. He develops de novo machine learning algorithms that increase the accuracy and precision of quantitative, integrative analyses of multi-modal brain images. His approaches combine data-driven training & clinician/scientist expert prior knowledge and have both clinical and neuroscience applications. Clinical applications include automating image interpretation for diagnoses and prognoses (assistive second reader), optimizing non-invasive treatments (e.g. tDCS, TMS), automatic clean-up of data, and automatic feature extraction from images. Basic science applications include understanding brain activity in health and in neurological disorders.

Researchers in Dr. Montillo’s group focus their efforts on four areas. (1) Discovering the significant assoicaitons between non-invasive functional and structural connectomics features (from fMRI, MEG/EEG and diffusion MRI) and disease severity and treatment outcomes in pediatric radiology, TBI, epilepsy, autism, diabetes, schizophrenia and Alzheimer’s. (2) Uncovering associations between radiological imaging signatures (such as anatomical and structural connectomics from MRI) and gene expression and optimizing their combined use in prognostics. (3) Developing novel machine learning methods (such as deep learning neural networks) for personalized medicine. (4) Developing multi-parametric MRI for improved cancer diagnostics and prognostics.

Dr. Montillo is actively engaged in supervising, teaching and training of students spanning UT Southwestern, UT Dallas, and UT Arlington. He teaches Machine Learning at UTSW and at UT Dallas through an adjunct appointment there, serves on Ph.D. thesis committees at UT Arlington and UT Southwestern.

Undergraduate

Rensselaer Polytechnic Inst (1992), Computer Sciences

Graduate School

Rensselaer Polytechnic Inst (1993), Computer Sciences

Graduate School

University of Pennsylvania (2004), Medical Imaging

• Developing statistical, machine learning approaches that extract radiological imaging (MEG/EEG, functional, diffusion, perfusion, metabolic MRI) and imaging-genomic biomarkers and that engender personalized diagnostics and prognostics in clinical neuroscience and oncological applications.
• Testing novel candidate therapies with such biomarkers for improved patient care. Developing novel algorithmic methods to stratify patients through retrospective data analyses that guide prospective patient care.

Featured Publications

Predicting response to the antidepressant bupropion using pretreatment fMRI

Nguyen KP, Fatt CC, Treacher A, Mellema C, Trivedi MH, Montillo A Medical Image Computing and Computer-Assisted Intervention 2019

Sensitivity of Derived Clinical Biomarkers to rs-fMRI Preprocessing Software Versions

Nguyen KP, Fatt CC, Mellema C, Trivedi MH, Montillo A IEEE International Symposium on Biomedical Imaging 2019 1 1581-1584

Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture

Treacher A, Beauchamp D, Quadri B, Vij A, Fetzer D, Yokoo T, Montillo A Medical Imaging: Computer-Aided Diagnosis (SPIE) 2019 1 109503E1-8

Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI

Mellema C, Treacher A, Nguyen KP, Montillo A IEEE International Symposium on Biomedical Imaging 2019 1 1891-1895

Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective

Dash D, Ferrari P, Malik S, Montillo A, Maldjian J, Wang J Brain Informatics 2018 1 163-172

Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography

Prabhat Garg, Elizabeth M. Davenport, Gowtham Murugesan, Christopher Whitlow, Joseph Maldjian, Albert Montillo MICCAI 2017

Automatic Multiple MEG Artifact Detection using 1-D Convolutional Neural Networks without Electrooculography or Electrocardiography

Prabhat Garg, Elizabeth Davenport, Gowtham Murugesan, Ben Wagner, Christopher Whitlow, Joseph Maldjian, Albert Montillo PRNI 2017

Single Season Changes in Resting State Network Power and the Connectivity between Regions Distinguish Head Impact Exposure Level in High School and Youth Football Players

Gowtham Murugesan, Behrouz Saghafi, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alex Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo SPIE Medical Imaging 2017

Single football season changes in resting state fMRI of the Default Mode Network and hippocampal regions correlate with Visuospatial Inhibitory Attention Performance in Youth and High School Players

Gowtham Murugesan, Thomas O Neil, Afarin Famili, Elizabeth M Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo ISBI 2018

Quantifying the Association between White Matter Integrity Changes and Subconcussive Head Impact Exposure from a Single Season of Youth and High School Football using 3D Convolutional Neural Networks

Behrouz Saghafi, Gowtham Murugesan, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alexander Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo SPIE Medical Imaging 2018

 Featured Books

Entanglement and Differentiable Information Gain Maximization. In Decision Forests for Computer Vision and Medical Image Analysis

Albert Montillo, Jilin Tu, Jamie Shotton, John Winn, J. Eugenio Iglesias, Dimitris Metaxas, and Antonio Criminisi (2013). Springer

Medical Computer Vision: Algorithms for Big Data, Fourth International MICCAI Workshop

Bjoern H. Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Muller, Shaoting Zhang, Weidong Cai, Dimitri Metaxas (Ed.) (2015). Springer

Medical Computer Vision: Large Data in Medical Imaging, Third International MICCAI Workshop

Bjoern Menze, Georg Langs, Albert Montillo, Henning Muller, Zhuowen Tu (2014). Springer

Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, Second International MICCAI Workshop

Bjoern Menze, Georg Langs, Le Lu, Albert Montillo, Zhuowen Tu, Antonio Criminisi (Ed.) (2013). Springer

• Organization for Human Brain Mapping (OHBM) (2012)
• American Society of Neuroradiology (ASNR) (2011)
• IEEE (senior member) (2002)
• Medical Image Computing and Computer assisted intervention (MICCAI) (2002)