About Me
I'm Rohan Chatterjee, a recent graduate of CSULA with a bachelors in computer science, with a strong passion for research and academia. In my final year, I achieved Summa Cum Laude honors and received the CSU-LSAMP-PROUD Scholar Award from the Louis Stokes Alliance for Minority Participation program.- University of California, Los Angeles (UCLA) : Sep 2024 - Current
- Master's in Data Science for Biomedicine
- California State University Los Angeles (CSULA) : Aug 2018 - May 2023
- Bachelor's in Computer Science
- Minors in Mathematics and Biomedical Engineering
- Graduated Summa Cum Laude
- T. Hong, F. Mohammadi, R. Chetterjee, D. Sagar, F. Mohammadi, A. Vaishya, M. Pourhomayoun, M. Kaur, J. Soo Lim and N. Amini, “A Novel Similarity Measure for Retinal Optical Coherence Tomography Images”, Proc. of the International Symposium on Visual Computing (ISVC), Oct. 2021, pp. 761-772 (Acceptance Rate: 30%).
- R. Chatterjee, D. Sagar, F. Mohammadi, M. Pourhomayoun, M. Kaur and N. Amini “Deep Residual Distilled Convolutional Learning for Detection of Large Vessel Occlusion in Ischemic Stroke Patients”,, Proc. of the IEEE International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC 2024) (Acceptance Rate: 28%).
- A. Mousavian, J. Jarkaneh, M. Pourhomayoun, M. Kaur, R. Chatterjee, S. Besharati, K. Nouri-Mahdavi and N. Amini “Deep Learning Image Analysis of Macular Optical Coherence Tomography Angiography Images for Detection of Progression in Glaucoma”, Proc. of the IEEE International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC 2024) (Acceptance Rate: 28%).
AIMHC 2024 Best Paper - R. Chatterjee, D. Sagar, F. Mohammadi, A. Vaishya, M. Pourhomayoun, M. Kaur, J. Soo Lim, N. Amini, "Detection Of Large Vessel Occlusion In Ischemic Stroke Patients Using Deep Residual Distilled Convolutional Networks", accepted for publication in the International Journal of Semantic Computing, to be published in Late 2024.
- Assisting in research under Dr. Navid Amini at CSULA's Machine Learning and Sensing Lab
- Research intern at UCLA through the Bruins-In-Genomics program under Dr. Grace Xiao’s lab, Xiao Lab
- Recognized as an outstanding intern at the B.I.G. Summer award ceremony.
- Machine Learning and AI
- Deep Learning
- Medical Informatics
- Biomedical Image Processing
- Computational Biology
- Computer Vision
- Distinguished as the only undergraduate Instructional Teaching Assistant for the College of Engineering, Computer Science, and Technology
- Mentor for students in the Biomedical Engineering Women Innovators Program
- Lead CS Tutor at CSULA's Center for Academic Success
- Pursuing a PhD in Computer Science
- Research focus on Machine Learning and Medical Technology
My Papers
A Novel Similarity Measure for Retinal Optical Coherence Tomography Images
The International Symposium on Visual Computing (ISVC) 2021
T. Hong, F. Mohammadi, R. Chetterjee, E. Chan, M. Pourhomayoun, K. Nouri-Mahdavi, V. Mohammadzadeh and N. Amini
Abstract: Optical coherence tomography (OCT) has become an essential tool for imaging of the retina to perform diagnosis and management of ocular and non-ocular diseases. In this paper, we evaluate the performance of structural similarity (SSIM) and multiscale structural similarity (MS-SSIM) metrics in comparing cross-sectional OCT images of the retina. We will then propose new metrics that only consider areas with a high signal-to-noise ratio in retinal OCT images. Our experimental comparisons demonstrate that, as opposed to SSIM and MS-SSIM measurements, the proposed metrics, called ROI-SSIM and ROI-MS-SSIM, more effectively extract structural information from OCT images and hence, yield similarity values more reflective of human visual perception.
Deep Residual Distilled Convolutional Learning for Detection of Large Vessel Occlusion in Ischemic Stroke Patients
IEEE International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC 2024)
R. Chatterjee, D. Sagar, F. Mohammadi, M. Pourhomayoun, M. Kaur and N. Amini
Abstract: Acute ischemic strokes (AIS) caused by large vessel occlusion (LVO) have been shown to result in significant rates of post-stroke dependence and mortality. Rapid detection of LVOs is essential for timely treatment to mitigate long-term functional and psychological consequences. The standard approach for LVO detection involves manual analysis of computed tomography angiography (CTA) brain scans, often leading to treatment delays due to the limited availability of experienced radiologists. In this paper, we propose a novel Three-Dimensional Deep Residual Dilated Convolutional Neural Network (DRDCNet-3D) that utilizes the power of dilated convolutions to enhance the spatial resolution for rapid detection of LVOs directly from CTA brain scans. Using a recent publicly-available dataset, we demonstrate that our proposed model achieves an AUC-ROC of 0.91 and an F1-score of 0.90, which is, on average, a 20% increase from the currently available open-source state-of-the-art 3D image analysis models—demonstrating its effectiveness in the diagnosis of LVOs. Our approach can assist physicians in swiftly determining the optimal treatment strategies for patients with suspected acute ischemic stroke, mitigating the risk of permanent severe damage.
Deep Learning Image Analysis of Macular Optical Coherence Tomography Angiography Images for Detection of Progression in Glaucoma
IEEE International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC 2024).
A. Mousavian, J. Jarkaneh, M. Pourhomayoun, M. Kaur, R. Chatterjee, S. Besharati, K. Nouri-Mahdavi and N. Amini
AIMHC 2024 Best Paper
Abstract: Optical Coherence Tomography Angiography (OCTA) is a new, non-invasive imaging modality for visualizing retinal capillaries for the diagnosis and management of multiple eye-related diseases. Glaucoma is one such disease, caused by the loss of retinal ganglion cells, which if progressed and not treated in time, will eventually lead to blindness. In this study, we present an innovative approach based on Convolutional Neural Networks (CNNs) to detect glaucoma progression. Our progression detection method is composed of five major steps: vessel segmentation, thick vessel masking, Otsu thresholding, super-pixel extraction, and measurement of macular microvascular density. Microvascular density measurements within predefined super pixels are compared between baseline and follow-up OCTA scans of 10 glaucoma patients. Our analysis demonstrates the effectiveness of our approach in distinguishing patients with progressive glaucoma from those whose glaucoma is stable. Our study offers a comprehensive assessment of microvascular vessel density dropouts observed in glaucomatous eyes over time, which enables early detection of disease progression. With proper management and timely treatment for patients with progressive glaucoma, vision loss can be slowed or prevented.
Resume
My projects
Characterization of Allele-Specific Alternative Splicing & Expression Events in Schizophrenia
Bruins In Genomics Research Intern - Xiao Lab, UCLA
Description: RNA splicing is a highly regulated RNA processing mechanism, where introns are removed and exons are ligated to produce mature mRNA. Additionally, RNA splicing is a primary link between genetic variations and complex traits.3 RNA splicing is highly tissue specific, with a particularly high frequency and complex alternative splicing patterns in brain tissues. Many studies have shown that phenotypical changes in the prefrontal cortex are associated with schizophrenia. However, the genetic basis of dysregulated splicing in schizophrenia is not fully understood. Therefore, this study examined RNA-seq data from the Dorsolateral prefrontal cortex (DLPFC) of schizophrenia and control samples. We implemented the Allele Specific Alternative mRNA processing (ASARP) method, to identify genetically influenced Allele Specific Alternative Splicing (ASAS) events and Allele Specific Expression (ASE) events that contribute to schizophrenia. Our approach has identified several genes with allele specific expression and alternative splicing events. Our results provide an improved understanding of the genetically driven ASAS and ASE associated with schizophrenia.
Comorbidity and Genetic Factors and their Impacts on Patients with COVID-19
Capstone Senior Design program - CSULA
Description: The objective of this project is to study overlooked or unconsidered aspects related to COVID-19 to gain a better
understanding of the disease. This is being done by studying various areas, including clinical factors such as blood type and its association
with disease severity and mortality, as well as the impact of geographical, economical, and social factors on COVID-19 cases, vaccinations, and mortality.