Mohammad Arafat Hussain, PhD

Children's Hospital,

Landmark Center,

401 Park Dr, Boston,

MA 02115, USA

[Harvard Catalyst Profile] [HBI Connectome] [Boston Children’s]

I am currently a Postdoctoral Research Fellow in the Fetal-Neonatal Neuroimaging Developmental Science Center at Harvard Medical School, Boston, Massachusetts. My research interests lie in machine/deep learning and its application to medical image analysis.

Previously, I worked as a Postdoctoral Research Associate in the Medical Image Analysis Lab (MIAL) at Simon Fraser University, Burnaby, BC, Canada.

I completed my Ph.D. in Biomedical Engineering at the University of British Columbia (UBC), Vancouver. In my Ph.D. project, I primarily focused on kidney cancer detection and analysis in CT using deep learning. I also completed my M.A.Sc. in Biomedical Engineering at UBC, Vancouver. My M.A.Sc. project focused on robust bone boundary localization in ultrasound to facilitate minimally invasive ultrasound-guided orthopedic surgery.

Before joining UBC, I completed an M.Sc. and B.Sc. in Electrical and Electronic Engineering at Bangladesh University of Engineering & Technology (BUET). During my M.Sc., I worked as a Research Engineer and developed novel ultrasound elastography techniques for breast cancer detection. I also worked as a Software Engineer in the Samsung R&D Institute Bangladesh after completing my B.Sc. degree.

news

Jan 10, 2023	Paper accepted in Displays [Link]
Oct 1, 2022	Paper accepted in Computerized Medical Imaging and Graphics. [Link]
Sep 7, 2022	Abstract accepted in Nature AI Conference, Bonn, Germany. [Poster]
Jul 7, 2022	Paper accepted in Journal of Computational Science. [Link]
May 16, 2022	Abstract accepted in 60th Annual Meeting of ASNR, NY, USA.

selected publications

bioRxiv
Enhancing Neurocognitive Outcome Prediction in Congenital Heart Disease Patients: The Role of Brain Age Biomarkers and Beyond

Mohammad Arafat Hussain, Ellen Grant, and Yangming Ou

bioRxiv 2023

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This paper aimed to investigate the predictive power of combining demographic, socioeconomic, and genetic factors with a brain MRI-based quantified measure of accelerated brain aging (referred to as deltaAGE) for neurocognitive outcomes in adolescents and young adults with Congenital Heart Disease (CHD). Our hypothesis posited that including the brain age biomarker (deltaAGE) would enhance neurocognitive outcome predictions compared to models excluding it. We conducted comprehensive analyses, including leave-one-subject-out and leave-one-group-out cross-validation techniques. Our results demonstrated that the inclusion of deltaAGE consistently improved prediction performance when considering the Pearson correlation coefficient, a preferable metric for this study. Notably, the deltaAGE-augmented models consistently outperformed those without deltaAGE across all cross-validation setups, and these correlations were statistically significant (p-value < 0.05). Therefore, our hypothesis that incorporating the brain-age biomarker alongside demographic, socioeconomic, and genetic factors enhances neurocognitive outcome predictions in adolescents and young adults with CHD is supported by the findings.
@article{hussain2023enhancing, year = {2023}, doi = {https://doi.org/10.1101/2023.09.01.555976}, pages = {2023-02}, title = {Enhancing Neurocognitive Outcome Prediction in Congenital Heart Disease Patients: The Role of Brain Age Biomarkers and Beyond}, journal = {bioRxiv}, author = {Hussain, Mohammad Arafat and Grant, Ellen and Ou, Yangming}, publisher = {Cold Spring Harbor Laboratory} }
Preprints
Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

Abrar Morshed, Abdulla Al Shihab, Md Abrar Jahin, Md Jaber Al Nahian, Md Murad Hossain Sarker, Md Sharjis Ibne Wadud, Mohammad Istiaq Uddin, Muntequa Imtiaz Siraji, Nafisa Anjum, Sumiya Rajjab Shristy, and 1 more author

Preprints 2023

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The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.
@article{morshed2023ultrasound, doi = {https://doi.org/10.20944/preprints202303.0296.v3}, journal = {Preprints}, title = {Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies}, author = {Morshed, Abrar and Al Shihab, Abdulla and Jahin, Md Abrar and Al Nahian, Md Jaber and Sarker, Md Murad Hossain and Wadud, Md Sharjis Ibne and Uddin, Mohammad Istiaq and Siraji, Muntequa Imtiaz and Anjum, Nafisa and Shristy, Sumiya Rajjab and others}, year = {2023}, publisher = {Preprints} }
bioRxiv
Can deep learning predict human intelligence from structural brain MRI?

Mohammad Arafat Hussain, Danielle LaMay, Ellen Grant, and Yangming Ou

bioRxiv 2023

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Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. Nevertheless, population-level association does not fully account for individual variability in intelligence. To address this, individual prediction studies emerge recently. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject’s verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, FSIQ), which reflect both fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input images, six deep learning models, and two outcome settings, on 850 autistic and healthy subjects 6-64 years of age. Results show promise with statistical significance, and also open up questions inviting further future studies.
@article{hussain2023can, doi = {https://doi.org/10.1101/2023.02.24.529924}, title = {Can deep learning predict human intelligence from structural brain MRI?}, author = {Hussain, Mohammad Arafat and LaMay, Danielle and Grant, Ellen and Ou, Yangming}, journal = {bioRxiv}, pages = {2023--02}, year = {2023}, publisher = {Cold Spring Harbor Laboratory} }
bioRxiv
Influence of Demographic, Socio-economic, and Brain Structural Factors on Adolescent Neurocognition: A Correlation Analysis in the ABCD Initiative

Mohammad Arafat Hussain, Grace Li, Ellen Grant, and Yangming Ou

bioRxiv 2023

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The Adolescent Brain Cognitive Development (ABCD) initiative is a longitudinal study aimed at characterizing brain development from childhood through adolescence and identifying key biological and environmental factors that influence this development. The study measures neurocognitive abilities across a multidimensional array of functions, with a focus on the critical period of adolescence during which physical and socio-emotional changes occur and the structure of the cortical and white matter changes. In this study, we perform a correlation analysis to examine the linear relation of adolescent neurocognition functions with the demographic, socio-economic, and magnetic resonance imaging-based brain structural factors. The overall goal is to obtain a comprehensive understanding of how natural and nurtural factors influence adolescent neurocognition. Our results on > 10,000 adolescents show many positive and negative statistical significance interrelations of different neurocognitive functions with the demographic, socioeconomic, and brain structural factors, and also open up questions inviting further future studies.
@article{hussain2023influence, doi = {https://doi.org/10.1101/2023.02.24.529930}, title = {Influence of Demographic, Socio-economic, and Brain Structural Factors on Adolescent Neurocognition: A Correlation Analysis in the ABCD Initiative}, author = {Hussain, Mohammad Arafat and Li, Grace and Grant, Ellen and Ou, Yangming}, journal = {bioRxiv}, pages = {2023--02}, year = {2023}, publisher = {Cold Spring Harbor Laboratory} }
Preprints
Inferring Neurocognition and Intelligence using Brain MRI

Mohammad Arafat Hussain, Ellen Grant, and Yangming Ou

Preprints 2023

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Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.
@article{hussain2023inferring, doi = {https://doi.org/10.20944/preprints202302.0452.v1}, journal = {Preprints}, title = {Inferring Neurocognition and Intelligence using Brain MRI}, author = {Hussain, Mohammad Arafat and Grant, Ellen and Ou, Yangming}, year = {2023}, publisher = {Preprints} }
CMIG
Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT

Mohammad Arafat Hussain, Zahra Mirikharaji, Mohammad Momeny, Mahmoud Marhamati, Ali Asghar Neshat, Rafeef Garbi, and Ghassan Hamarneh

Computerized Medical Imaging and Graphics 2022

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Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.
@article{HUSSAIN2022102127, title = {Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT}, journal = {Computerized Medical Imaging and Graphics}, pages = {102127}, year = {2022}, issn = {0895-6111}, doi = {https://doi.org/10.1016/j.compmedimag.2022.102127}, author = {Hussain, Mohammad Arafat and Mirikharaji, Zahra and Momeny, Mohammad and Marhamati, Mahmoud and Neshat, Ali Asghar and Garbi, Rafeef and Hamarneh, Ghassan}, keywords = {Deep learning, Semi-supervised learning, Active learning, Segmentation, Noisy teacher, COVID-19, Pneumonia}, publisher = {Elsevier} }

ASNR

Accelerated Brain Aging in Congenital Heart Disease and Relation to Neurodevelopmental Outcome

Ruhika Lankalapalli, Sheng He, James Ko, Mohammad Arafat Hussain, Kiho Im, Ai Wern Chung, Patric Johnston, Jane Newburger, Sarah Morton, Ellen Grant, and 1 more author

In 60th Annual Meeting of the American Society of Neuroradiology 2022

Bib PDF

@inproceedings{lankalapalli2022accelerated,
  title = {Accelerated Brain Aging in Congenital Heart Disease and Relation to Neurodevelopmental Outcome},
  author = {Lankalapalli, Ruhika and He, Sheng and Ko, James and Hussain, Mohammad Arafat and Im, Kiho and Chung, Ai Wern and Johnston, Patric and Newburger, Jane and Morton, Sarah and Grant, Ellen and Ou, Yangming},
  booktitle = {60th Annual Meeting of the American Society of Neuroradiology},
  year = {2022},
  organization = {ASNR}
}

Nature Conf.

Machine Intelligence to Predict Human Intelligence

Mohammad Arafat Hussain, Danielle LaMay, Ellen Grant, and Yangming Ou

In AI, neuroscience and hardware: From neural to artificial systems and back again 2022

Bib PDF Poster

@inproceedings{hussain2022machine,
  title = {Machine Intelligence to Predict Human Intelligence},
  author = {Hussain, Mohammad Arafat and LaMay, Danielle and Grant, Ellen and Ou, Yangming},
  booktitle = {AI, neuroscience and hardware: From neural to artificial systems and back again},
  year = {2022},
  organization = {Nature Conferences}
}

CIBM
Learning-to-Augment Strategy using Noisy and Denoised Data: Improving Generalizability of Deep CNN for the Detection of COVID-19 in X-ray Images

Mohammad Momeny, Ali Asghar Neshat, Mohammad Arafat Hussain, Solmaz Kia, Mahmoud Marhamati, Ahmad Jahanbakhshi, and Ghassan Hamarneh

Computers in Biology and Medicine 2021

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Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive images, normal images, and other non-COVID pneumonia images, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method has the better performance in comparison with the data augmentation approaches in terms of sensitivity (by 0.808), specificity (by 0.915), and F-Measure (by 0.737).
@article{momeny2021learning, title = {Learning-to-Augment Strategy using Noisy and Denoised Data: Improving Generalizability of Deep CNN for the Detection of COVID-19 in X-ray Images}, author = {Momeny, Mohammad and Neshat, Ali Asghar and Hussain, Mohammad Arafat and Kia, Solmaz and Marhamati, Mahmoud and Jahanbakhshi, Ahmad and Hamarneh, Ghassan}, journal = {Computers in Biology and Medicine}, year = {2021}, publisher = {Elsevier} }
CMIG
Learnable Image Histograms-based Deep Radiomics for Renal Cell Carcinoma Grading and Staging

Mohammad Arafat Hussain, Ghassan Hamarneh, and Rafeef Garbi

Computerized Medical Imaging and Graphics 2021

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Fuhrman cancer grading and tumor-node-metastasis (TNM) cancer staging systems are typically used by clinicians in the treatment planning of renal cell carcinoma (RCC), a common cancer in men and women worldwide. Pathologists typically use percutaneous renal biopsy for RCC grading, while staging is performed by volumetric medical image analysis before renal surgery. Recent studies suggest that clinicians can effectively perform these classification tasks non-invasively by analysing image texture features of RCC from computed tomography (CT) data. However, image feature identification for RCC grading and staging often relies on laborious manual processes, which is error prone and time-intensive. To address this challenge, this paper proposes a learnable image histogram in the deep neural network framework that can learn task-specific image histograms with variable bin centers and widths. The proposed approach enables learning statistical context features from raw medical data, which cannot be performed by a conventional convolutional neural network (CNN). The linear basis function of our learnable image histogram is piece-wise differentiable, enabling back-propagating errors to update the variable bin centers and widths during training. This novel approach can segregate the CT textures of an RCC in different intensity spectra, which enables effcient Fuhrman low (I/II) and high (III/IV) grading as well as RCC low (I/II) and high (III/IV) staging. The proposed method is validated on a clinical CT dataset of 159 patients from The Cancer Imaging Archive (TCIA) database, and it demonstrates 80% and 83% accuracy in RCC grading and staging, respectively.
@article{hussain2021learnable, title = {Learnable Image Histograms-based Deep Radiomics for Renal Cell Carcinoma Grading and Staging}, author = {Hussain, Mohammad Arafat and Hamarneh, Ghassan and Garbi, Rafeef}, journal = {Computerized Medical Imaging and Graphics}, year = {2021}, publisher = {Elsevier} }
IEEE TMI
Cascaded Localization Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation

Mohammad Arafat Hussain, Ghassan Hamarneh, and Rafeef Garbi

IEEE Transactions on Medical Imaging 2021

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Kidney volume is an essential biomarker for a number of kidney disease diagnoses, for example, chronic kidney disease. Existing total kidney volume estimation methods often rely on an intermediate kidney segmentation step. On the other hand, automatic kidney localization in volumetric medical images is a critical step that often precedes subsequent data processing and analysis. Most current approaches perform kidney localization via an intermediate classification or regression step. This paper proposes an integrated deep learning approach for (i) kidney localization in computed tomography scans and (ii) segmentation-free renal volume estimation. Our localization method uses a selection-convolutional neural network that approximates the kidney inferior-superior span along the axial direction. Cross-sectional (2D) slices from the estimated span are subsequently used in a combined sagittal-axial Mask-RCNN that detects the organ bounding boxes on the axial and sagittal slices, the combination of which produces a final 3D organ bounding box. Furthermore, we use a fully convolutional network to estimate the kidney volume that skips the segmentation procedure. We also present a mathematical expression to approximate the ‘volume error’ metric from the ‘Sørensen–Dice coefficient.’ We accessed 100 patients’ CT scans from the Vancouver General Hospital records and obtained 210 patients’ CT scans from the 2019 Kidney Tumor Segmentation Challenge database to validate our method. Our method produces a kidney boundary wall localization error of 2.4mm and a mean volume estimation error of 5%.
@article{hussain2021cascaded, title = {Cascaded Localization Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation}, author = {Hussain, Mohammad Arafat and Hamarneh, Ghassan and Garbi, Rafeef}, journal = {IEEE Transactions on Medical Imaging}, year = {2021}, publisher = {IEEE} }