Bioimaging and Biomedical Optics Commons^™

Single Fluorogens And Orientation-Localization Microscopy For Quantifying Chemical And Biomolecular Dynamics At The Nanoscale, Yiyang Chen, Yuanxin Qiu, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Many chemical systems look uniform only because ensemble measurements average over their most interesting molecules. Electron-transfer rates vary across electrode surfaces; lipid membranes contain nanodomains with distinct packing and fluidity; peptide aggregates exhibit local polymorphism; and biomolecular condensates contain transient networks of interactions that are blurred in ensemble images. A central challenge in chemical imaging is not simply to see smaller structures, but to measure chemical variables such as polarity, redox potential, and molecular confinement at the single-molecule level. In this Account, we describe how fluorogens, molecules whose brightness, blinking, spectral shifts, orientation, rotational mobility, and motion are directly shaped …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Characterizing Stiffness Dynamics Of Normal And Malignant Breast Spheroids Using Brillouin Microscopy, Razanne Rafat Zaghloul, Karlin Hilai, Chenjun Shi, Jitao Zhang

Medical Student Research Symposium

Background: Breast cancer progression and metastasis are closely linked to alterations in the mechanical properties of tumor cells and their microenvironment. Softer, more deformable cells are often associated with higher metastatic potential. While atomic force microscopy (AFM) is the current gold standard for mechanical characterization, it is limited to surface measurements and can damage 3D cultures. It remains unclear how the mechanical properties evolve over time in normal versus malignant spheroids. This study utilizes Brillouin light-scattering microscopy, a non-contact and label-free optical technique, to assess stiffness changes in normal and malignant breast epithelial spheroids over time. Understanding these mechanical signatures …

Sensing Negative-Cone Rotational Diffusion Of Dipole-Like Emitters, Yuanxin Qiu, Kaizhi A. Nie, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Fluorescence anisotropy and single-molecule orientation-localization microscopy (SMOLM) are powerful techniques that quantify the rotational diffusion of dipole-like emitters, which is important for sensing molecular interactions and chemical environments at the nanoscale. Numerous theoretical and experimental studies have thoroughly characterized single-molecule rotations even when those rotations are much faster than the detector integration time. Here, we extend the theory of measuring rotational diffusion to situations where a single dipole rotates uniformly everywhere outside of an isotropic cone of a certain size, termed a negative cone. This scenario corresponds to negative fluorescence anisotropy 𝑟 and has been observed in emitters exhibiting strong …

Adversarial Robustness In Biomedical Time-Series Models, Rohan Tiwari

Bioengineering Theses

This study investigates adversarial vulnerabilities in deep learning models for biomedical time-series classification across two clinically important modalities: electrocardiography (ECG) and electroencephalography (EEG). Using the MIT-BIH Arrhythmia and CHB-MIT seizure datasets, I evaluate time-domain attacks (FGSM, PGD), Fourier-domain constrained attacks, and learned spectral perturbations designed to reveal modality-specific sensitivity patterns. Across both tasks, a consistent trend emerges low-frequency components (0–5 Hz) constitute a dominant axis of adversarial vulnerability, with perturbations in this range producing the steepest degradation in classification performance. In ECG models, protecting the physiologically relevant QRS band (5–20 Hz) significantly improves robustness, whereas EEG models remain highly sensitive …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Volumetric Fluorescence Microscopy For High-Throughput And High-Sensitivity Imaging: From Single Molecules To Tissues, Le-Mei Wang

Graduate Studies Theses and Dissertations 2026

Fluorescence microscopy is an indispensable tool in the biological sciences, enabling researchers to investigate intricate subcellular structures, particularly for volumetric studies. However, conventional optical microscopy for volumetric imaging remains fundamentally constrained by imaging speed and throughput. To bypass traditional serial z-scanning, we introduce an axially scan-free method using a phase layer cake to modulate the system's point spread function. This approach projects volumetric information onto a 2D plane in a single shot, offering high flexibility in tuning axial depth alongside simultaneous multicolor imaging with high spatial resolution and sensitivity. This dissertation divides these technical advancements into cellular and tissue imaging …

On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations, Kevin Mark Garner Jr.

Computer Science Theses & Dissertations

Mesh generation is a critical component in numerical approximations of Partial Differential Equations (PDEs). One such example includes Computational Fluid Dynamics (CFD), as CFD simulations in turn are crucial for applications in many industries, such as personalized healthcare and the design of aerospace vehicles. Generating high quality meshes for large-scale CFD problems presents a significant bottleneck in the CFD workflow. This dissertation proposes “fast,” parallel 3D mesh generation methodologies that are designed to leverage the concurrency offered by emerging High-Performance Computing (HPC) architectures. First, a distributed memory method is presented that integrates a sequential state-of-the-art isotropic, advancing front local reconnection-based …

Single-Molecule Orientation And Localization Microscopy, Sophie Brasselet, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Single-molecule localization microscopy (SMLM) offers enhanced spatial resolution in optical microscopy, providing detailed insights into the spatial organization of proteins in cells at the nanoscale. Over the past decade, SMLM has progressively incorporated the capability to retrieve the orientations of single molecules using their polarized dipolar emission pattern. Here we explore recent advancements in single-molecule orientation and localization microscopy (SMOLM), which yields super-resolved images of molecular three-dimensional (3D) orientations, wobble and 3D positions. This advancement opens possibilities to explore the nanoscale organization and conformation of biological molecules as well as to monitor and design local 3D optical fields in nanophotonics. …

Low-Cost Cutaneous Protoporphyrin Ix (Ppix) Detection (Cpd) Device For Follow-Up Monitoring Of Patients After Photodynamic Therapy, Md Asaduzzaman Rasel

Graduate Masters Theses

Background: Photodynamic Therapy (PDT) utilizes specific wavelengths of light to activate photosensitizing chemical compounds, known as photosensitizers, which induce the generation of cytotoxic reactive oxygen species (ROS) for the targeted destruction of cancer cells. Among various photosensitizers for PDT, Protoporphyrin IX (PpIX) is widely employed in oncology and dermatology due to its natural in situ generation via the metabolic conversion of 5- aminolevulinic acid (ALA), a non-phototoxic prodrug. Systemic administration of ALA after 3-6 hr drug delay leads to peak PpIX accumulation in tissues, facilitating therapeutic and diagnostic applications. However, PpIX can persist in the skin for 24–48 hours post-treatment, …

Design And Development Of A Standalone Digital Holographic Microscope Employing Phase-Driven Reconstruction And Classification For Biomedical Imaging And Optical Diagnostics, Charlotte Kyeremah

Graduate Doctoral Dissertations

Access to advanced biomedical imaging technologies remains a significant challenge in resource-limited settings, especially for early disease detection and monitoring of diseases such as malaria, HIV, and other blood-borne diseases. Although point-of-care (POC) devices have gained popularity in global health, many rely on antibody-based tests, lateral flow strips, or optical readouts that often lack quantitative capabilities, sensitivity to early infections, or versatility in different diagnostic targets. In addition, these systems are typically dependent on disposable reagents or manual interpretation, which limits their effectiveness in remote areas. Digital Holographic Microscopy (DHM) presents a promising alternative as a label-free imaging method capable …

Volumetric Imaging Of Cellular Dynamics Using Light-Sheet Microscopy: From Subcellular Structures To Whole Organisms, Md Nasful Huda Prince

Optical Science and Engineering ETDs

Advancing biomedical imaging requires platforms that combine high-resolution, speed, and stability across biological scales. This dissertation presents three innovations designed to address limitations of conventional light-sheet and oblique plane imaging. First, we introduce Reflected Inline Detection in Epi-Oblique Plane Microscopy (RIDE-OPM), a compact and drift-resistant system that eliminates a tertiary detection path while maintaining alignment flexibility, enabling robust long-term live imaging. Second, we present a high-speed Axially Swept Light-Sheet Microscopy (ASLM) platform for cleared tissues, using dual foci and synchronized sweeping to achieve isotropic sub-micron resolution at up to 40 frames-per-second, a fourfold improvement over standard ASLM. Integrated with a …

Multi-Modal Mri For The Pre-Clinical Evaluation Of Hiv-Linked Changes In The Central Nervous System, Gabriel Gauthier

Theses & Dissertations

Though the inception of anti-retroviral therapy (ART) has fundamentally changed the life expectancy of human immunodeficiency virus (HIV) patients, accessibility and outcomes vary enormously along demographic lines. Even for people living with HIV (PLWH) with perfect access and adherence to anti-retroviral drugs (ARVs), modern treatment plans are fundamentally unable to secure complete viral remission for patients. The cause of this perpetual infection is the persistence of viral reservoirs in the central nervous system (CNS), hidden beyond the reach of modern ARVs, leaving patients to deal with lifelong medication side-effects and risks of developing HIV-associated neurodegenerative disorders (HAND). Despite improving outcomes …

Impact Of Shear Flow On Marine Biofilms Using Hyperspectral Imaging, Shantanu Mahesh Kore

All Theses

Various microbial communities in marine biofilms cause biofouling on submerged surfaces, posing challenges to marine industries. Despite their ecological and economic importance, biofilms' biomechanical and biochemical responses to hydrodynamic shear stress are insufficiently understood, particularly in dynamic flow conditions. To fill this gap, our study uses an innovative fiber-optic hyperspectral imaging (HSI) system and a supercontinuum laser source to examine marine biofilms' structure, composition, and detachment behavior at different shear stress levels.

To detect spectral and spatial heterogeneity in live biofilms without labeling, we created a custom imaging pipeline that captures reflectance spectra in the 600-850 nm range, targeting microbial …

Painting Rich Six-Dimensional Pictures Using Polarized Fluorescence Microscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

No abstract provided.

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …

Explorations Of Dna-Single-Walled Carbon Nanotube Interactions To Develop Multiplexed Molecularly Specific Biosensors For Inflammation, Amelia K. Ryan

Dissertations and Theses

Inflammatory cytokines such as interleukin-6 (IL-6) and interleukin-12 (IL-12) are central regulators of immune signaling and key biomarkers of disease, yet existing assays for their detection remain slow, invasive, and lack multiplexing ability. This dissertation advances the development of single-walled carbon nanotube (SWCNT) optical nanosensors capable of real-time, multiplexed, and molecularly specific cytokine detection through innovative use of single-stranded DNA (ssDNA) interfaces.

First, an IL-6-specific DNA aptamer was employed as both a dispersing agent and recognition probe for SWCNT fluorescence sensing. Sequence modifications, including anchor domains, truncations, and thermally induced refolding, were systematically tested to optimize sensitivity and selectivity. The …

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

Automation To Autonomy: Temporal Dynamics Of Trust And Visual Attention Allocation Did Not Evolve, Tetsuya Sato, Eric Chancey, Yusuke Yamani

Psychology Faculty Publications

Emerging work environments are expected to implement autonomy that performs various functions without human input. Previous works has shown that trust in automation is negatively correlated with visual attention allocation, indicating that trust is a dynamic construct. Moreover, trust in automation and trust in autonomy appears to evolve in similar ways. However, recent work has demonstrated differences between trust in automation and trust in autonomy within Kaber’s (2018) theoretical framework (Sato et al., 2023b). Yet, it is uncertain whether the development of trust and visual attention allocation differs between automation and autonomy. The present study examined the temporal dynamics of …

Single-Molecule Orientation Imaging Reveals The Nano-Architecture Of Amyloid Fibrils Undergoing Growth And Decay, Brian Sun, Tianben Ding, Weiyan Zhou, Tara S. Porter, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Amyloid-beta (Aβ42) aggregates are characteristic Alzheimer’s disease signatures, but probing how their nanoscale architectures influence their growth and decay remains challenging using current technologies. Here, we apply time-lapse single-molecule orientation-localization microscopy (SMOLM) to measure the orientations and rotational “wobble” of Nile blue (NB) molecules transiently binding to Aβ42 fibrils. We correlate fibril architectures measured by SMOLM with their growth and decay over the course of 5 to 20 min visualized by single-molecule localization microscopy (SMLM). We discover that stable Aβ42 fibrils tend to be well-ordered and signified by well-aligned NB orientations and small wobble. SMOLM also shows that increasing order …

Evaluating Neuroimaging Modalities In The A/T/N Framework: Single And Combined Fdg-Pet And T1-Weighted Mri For Alzheimer’S Diagnosis, Peiwang Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

With the escalating prevalence of dementia, particularly Alzheimer's Disease (AD), the need for early and precise diagnostic techniques is rising. This study delves into the comparative efficacy of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and T1-weighted Magnetic Resonance Imaging (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently outperforms T1w-MRI across …

Resolving The Nanoscale Structure Of Β-Sheet Peptide Self-Assemblies Using Single-Molecule Orientation–Localization Microscopy, Weiyan Zhou, Conor L. O'Neill, Tianben Ding, Oumeng Zhang, Jai S. Rudra, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Synthetic peptides that self-assemble into cross-β fibrils are versatile building blocks for engineered biomaterials due to their modularity and biocompatibility, but their structural and morphological similarities to amyloid species have been a long-standing concern for their translation. Further, their polymorphs are difficult to characterize using spectroscopic and imaging techniques that rely on ensemble averaging to achieve high resolution. Here, we utilize Nile red (NR), an amyloidophilic fluorogenic probe, and single-molecule orientation-localization microscopy (SMOLM) to characterize fibrils formed by the designed amphipathic enantiomers KFE8^L and KFE8^D and the pathological amyloid-beta peptide Aβ42. Importantly, NR SMOLM reveals the helical (bilayer) …

6d Single-Fluorogen Orientation-Localization Microscopy For Elucidating The Architecture Of Beta-Sheet Assemblies And Biomolecular Condensates, Tingting Wu, Weiyan Zhou, Jai S. Rudra, Rohit V. Pappu, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

We develop six-dimensional single-molecule orientation-localization microscopy (SMOLM) to measure the 3D positions and 3D orientations simultaneously of single fluorophores. We show how careful optimization of phase and polarization modulation components can encode phase, polarization, and angular spectrum information from each fluorescence photon into a microscope’s dipole-spread function. We used the transient binding and blinking of Nile red (NR) to characterize the helical structure of fibrils formed by designed amphipathic peptides, KFE8^L and KFE8^D, and the pathological amyloid-beta peptide Aβ42. We also deployed merocyanine 540 to uncover the interfacial architectures of biomolecular condensates.

Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram

Graduate Theses, Dissertations, and Problem Reports (ETD)

Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

Unlv Title Iii Aanapisi & Mcnair Scholars Institute Research Journal 2024, Jesica Godinez-Paredes, Kian Hassankhan, Apia Hickman, Robin Ruth Kee, Kevin Ayala Pineda, Briana Melendez, Kalli Ramos, Saturn L. Reyes, Fabian Leija, Selena Pepe, Silva Topchyan, Celeste Ainsley, Mayra Arzate, Jessica Balistreri, Zantana Ephrem, Mirella Jasso, Karl Panou, Nichole Pelaez, Miklo Alcala, Akshay Dave, Mey Mey Heng, Michael Finkle, Janessa Montenegro, Reynafe Naol Aniga, Victor Mejia, Angelica G. Diaz, Anayeli Flores-Garibay, Kari Lee Joe Goold, Samantha Hernandez, Lianelys Cabrera Martinez, Caitlin Reynolds, Kimberly N. Usbeck, Abdulrahman Alahdal, Angelica Diaz, Willaine Mae Kahano, Raquel Jackson, Cecia Ruiz-Hernandez, Kers Ung-Watson, Yessenia Henriquez, Vanessa Marie Booth, Alexia Brown, Darlyn Magana, Bianca Navarro, Nicholas Pereira, Gia Renemae Calip, Lucky Heng, Ralph Sagun, Lucas Abreu, Alexandra Maria Acosta, Tristan Benally, Cosset Hernandez Pena, Adrian Montenegro, Cierra Paaaina-Daquioag, Nicole Torosian, Tracy Fuentes, Julissa Martinez, Medina Mcallister, Michal Newhouse-Van Vlerin, Tiria Carr, Nima Abkenar, Isabella Aceituno, Yuhan Bi, Victoria Campos, Melika Cummings, Zachary J. Johnigan, Alexis Sotolongo-Marin

McNair Journal

Journal articles based on research conducted by undergraduate students in the AANAPISI, LSAMP, and McNair Scholars Program.

Table of Contents

About AANAPISI

Dr. Chris Heavey, Interim President

Dr. Keith Rogers, Vice President for Student Affairs

Ms. Zhanna Aronov, Associate Vice President for Retention & Outreach

Don't Fear The Artificial Intelligence: A Systematic Review Of Machine Learning For Prostate Cancer Detection In Pathology, Aaryn Frewing, Alexander B. Gibson, Richard Robertson, Paul Urie, Dennis Della Corte

Faculty Publications

The adoption of whole slide image (WSI) scanners in clinical practice was accelerated by US Food and Drug Administration approval in 2017, which allowed primary pathologic diagnoses to be made on scanned images. Images in the digital domain allow the application of pathology artificial intelligence (AI), including clinical decision support with algorithms performing specific diagnoses.1,2 These algorithms, if trained properly, could go beyond the ability of human observation to detect and quantify features that are not recognizable by human perception.1,3,4

Synthesis, Radiolabeling And Evaluation Of A Suite Of Tracers With 44Sc For Detecting Extracellular Dna, Zhiyao Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neutrophil extracellular traps involve the rapid translocation of DNA to the outside of the cell under certain stimuli. This structure forms a fibrous network that is able to limit the spread of pathogens and to kill microorganisms. It has also been shown to be present in various pathological processes such as inflammation, autoimmune diseases, and cancer metastasis. Currently, the formation process of NETs in vivo is being extensively studied. However noninvasive detection and quantitation has yet to be achieved. A class of PET tracers are described here that consists of a DNA dye as the backbone that is labeled with …

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …