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1. RCMDD: A Denoising Architecture for Improved Recovery of Reflectance Confocal Microscopy Images of Skin from Compressive Samples

   https://doi.org/10.1109/HealthCom46333.2019.9009434  

   Arias, Fernando X.; Sierra, Heidy; Arzuaga, Emmanuel (February 2020, 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom))

   he Compressive Sensing (CS) framework has demonstrated improved acquisition efficiency on a variety of clinical applications. Of interest to this work is Reflectance Confocal Microscopy (RCM), where CS can influence a drastic reduction in instrumentation complexity and image acquisition times. However, CS introduces the disadvantage of requiring a time consuming and computationally intensive process for image recovery. To mitigate this, the current document details our preliminary work on expanding a Deep-Learning architecture for the acquisition and fast recovery of RCM images using CS. We show preliminary recoveries of RCM images of both a synthetic target and heterogeneous skin tissue using a state-of-the-art network architecture from compressive measurements at various undersampling rates. In addition, we propose an application-specific addition to an established network architecture, and evaluate its ability to further increase the accuracy of recovered CS RCM images and remove visual artifacts. Our initial results show that it is possible to recover compressively sampled images at near-real time rates with comparable quality to established computationally intensive and time-consuming optimization-based methods common in CS applications 

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2. Fluorescence excitation-scanning hyperspectral imaging with scalable 2D–3D deep learning framework for colorectal cancer detection

   https://doi.org/10.1038/s41598-024-64917-5  

   Oswald, Willaim; Browning, Craig; Yasmin, Ruthba; Deal, Joshua; Rich, Thomas C; Leavesley, Silas J; Gong, Na (December 2024, Scientific Reports)

   Abstract Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians. 

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3. Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

   https://doi.org/10.1117/12.2290696  

   Leavesley, Silas J.; Deal, Joshua; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rich, Thomas C.; Boudreaux, Carole W.; Rider, Paul F.; Hill, Shante (February 2018, Proc. SPIE 10489, Optical Biopsy XVI: Toward Real- Time Spectroscopic Imaging and Diagnosis, 104890K)

   Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices – likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied. 

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4. Decoding the Hidden: Direct Image Classification Using Coded Aperture Imaging

   https://doi.org/10.1109/CAMSAP58249.2023.10403419  

   Munoz, Jocelyn Ornelas; Rutter, Erica M.; Marcia, Roummel F. (December 2023, 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))

   Coded aperture imaging has emerged as a solution to enhance light sensitivity and enable imaging in challenging conditions. However, the computational expense of image reconstruction poses limitations in processing efficiency. To address this, we propose a direct classification method using convolutional neural networks. By leveraging raw coded measurements, our approach eliminates the need for explicit image reconstruction, reducing computational overhead. We evaluate the effectiveness of this approach compared to traditional methods on the MNIST and CIFAR10 datasets. Our results demonstrate that direct image classification using raw coded measurements achieves comparable performance to traditional methods while reducing computational overhead and enabling real-time processing. These findings highlight the potential of machine learning in enhancing the decoding process and improving the overall performance of coded aperture imaging systems. 

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5. Fast and Cost-Effective Three-Dimensional Microwave Imaging Using a Cylindrical Setup

   Amineh, Reza K.; Ravan, Maryam (August 2021, Applied Computational Electromagnetics Society)

   null (Ed.)

   In this paper, we propose a fast and low-cost cylindrical microwave imaging system based on the use of arrays of transmitter and receiver antennas and a customized low-cost data acquisition circuit using off-the-shelf components. The complex-valued scattered data captured with the proposed system is processed using near-field holographic image reconstruction. To enhance this technique, standardized minimum norm (SMN) approach is employed to solve the relevant systems of equations. The performance of the proposed imaging technique and the data acquisition system is demonstrated via simulations and experiments. 

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