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Nuclei Segmentation is a key step in understanding the distribution, size, and shape of nuclei in the underlying tissue. Traditionally, pathologists view histology slides under the microscope to analyze the nuclear structure. However, this process is time-consuming and is prone to inter-reader variability. An AI-based segmentation algorithm can aid pathologists in cancer detection and prognosis, and help speed up the cancer screening procedure. 

While there are several deep learning methods addressing this problem, we propose a Green Nuclei Segmentation algorithm that uses a simple, reliable, and modular approach to delineate nuclei in a histopathology slide. The Green U-shaped Learning(GUSL) is a 4 level pipeline that involves three main modules: representation learning using PixelHop, feature selection using RFT, and supervised learning using XGBoost Regressor. The different levels help look at the histopathology image at multiple resolutions, while we attempt to segment the nuclei in a coarse to fine manner. At each level, we aim to correct the previous layer’s predictions through residue correction. While this model gives good results, we can further improve the performance by refining the boundary regions to yield precise nuclei contours. 

Image denoising is a computer vision technique that removes noise from images while preserving essential structures and textures. It plays a critical role in applications such as photography enhancement, medical imaging, and remote sensing.

To address such problems, we have employed GUSL, a Green Learning-based pipeline tailored for image denoising. Noisy images are resized to multiple resolutions, and Green Learning techniques such as PixelHop, RFT, and LNT are applied at each level to extract features independently. Each level progressively refines the denoising result by correcting the residuals from the previous level. While this approach yields promising results, further refinement is needed to enhance performance in smooth and texture-rich regions.

Enhancing image feature extraction to boost image classification accuracy has been a significant research focus at the MCL lab. Initially, PixelHop++ was developed to efficiently extract image features and perform accurate image classification. Subsequently, the Least-Squares Normal Transform (LNT) was introduced to further enhance image features, improving classification results with PixelHop++ on standard image databases such as MNIST and FMNIST. Despite achieving commendable performance, further refinements remain desirable to push accuracy limits even higher.

To address this, we propose a novel pipeline of four distinct experimental setups involving different pooling strategies—absolute maximum pooling and variance pooling—at hops 1 and 2. We extract LNT features specifically from hops 1, 3, and 4 for each experiment. At hop-1, pooling (either max or variance) generates 10 LNT features per channel, resulting in a total of 250 features. Hop-3 involves transforming the (N, 3, 3, Feature) tensor to produce 90 LNT features. From hop-4, 10 additional LNT features are acquired following a DFT-based feature selection. These 350 LNT features from hops 1, 3, and 4 are concatenated alongside selected hop-4 features. Finally, features aggregated from all four experimental setups are combined, and a 10-class classifier is trained on these comprehensive feature sets, demonstrating an improvement in classification performance.

Image dehazing plays a crucial role in digital imaging by removing atmospheric distortions such as haze and fog, thereby enhancing scene clarity for applications ranging from photography to autonomous driving. Traditionally, methods like the Dark Channel Prior (DCP) have been used to estimate haze effects, leveraging the observation that most non-hazy images contain very dark pixels in at least one color channel.

In a significant advancement, researchers have now introduced a novel approach that combines the strength of DCP with the efficiency of the GUSL pipeline. In this new method, DCP serves as the foundational technique to provide an initial estimate of the haze, while the GUSL pipeline is employed to predict and correct the residual errors left by the DCP. This two-step process refines the dehazing process by capturing subtle details that DCP alone might miss.

The GUSL pipeline utilizes unsupervised representation learning for robust feature extraction, followed by supervised feature learning to enhance computational efficiency and output quality. This approach not only improves the overall dehazing performance but also maintains a lightweight design suitable for real-time applications on resource-constrained devices.

By integrating DCP with residue prediction through GUSL, the new method delivers superior image clarity with reduced computational overhead, making it an attractive solution for modern imaging challenges in mobile and edge computing environments.

Demosaicing is a critical process in digital imaging. Since each pixel on a typical sensor captures only one color channel, red, green, or blue, the complete color image must be reconstructed from incomplete data. Conventional approaches, including deep learning models, have made impressive strides in quality, yet their significant computational requirements often limit their deployment on resource-constrained edge devices.Mahtab Movahhedrad of the MCL Lab has introduced an innovative approach to digital imaging that promises to transform how devices reconstruct full-color images from partial sensor data. The new method, dubbed green U-shaped image demosaicing (GUSID), leverages green learning (GL) principles to offer a lightweight, transparent, and efficient alternative to traditional, computationally heavy deep learning techniques. GUSID takes a distinct path. Instead of relying on deep neural networks, it uses unsupervised representation learning for robust feature extraction, followed by supervised feature learning to enhance computational efficiency and maintain high-quality output. This dual-stage process allows GUSID to minimize computational overhead while delivering competitive accuracy. Its compact design and support for parallelized training make it particularly well-suited for real-time vision applications on devices with limited processing capabilities. As digital imaging continues to evolve, breakthroughs like GUSID not only enhance performance but also pave the way for future innovations in edge computing and real-time processing. The MCL Lab is poised to lead this exciting frontier, proving that sometimes, a smarter, leaner approach can make all the difference.