News

We are very happy to welcome a new MCL member, Qi Cao. Here is a quick interview with Qi:

1. Could you briefly introduce yourself and your research interests?

My name is Qi Cao, and I’m currently a PhD student in the ECE department at USC. I joined the MCL at the end of 2024. My research interests span machine learning, computer vision, and time-series analysis. Since joining MCL, I’ve been eager to contribute to ongoing projects, collaborate with lab members who have diverse technical backgrounds, and continue refining my skills through both hands-on experiments and regular discussions.

2. What is your impression of MCL and USC?

MCL comes across as a supportive, close-knit lab where the collaborative atmosphere helps make challenging projects feel manageable. USC strikes me as the kind of campus that buzzes with energy—entrepreneurial, diverse, and always open to new perspectives.

3. What is your future expectation and plan in MCL?

I’m looking forward to collaborating closely with everyone in MCL,  sharing ideas during group meetings, and learning from the lab’s diverse expertise. I hope to contribute actively to interesting projects by participating in regular discussions and offering support whenever it’s needed. I aim to both grow my own skills and help the whole team move our research forward.

Congratulations to Wei Wang for passing her defense today. Wei’s thesis title is “Explainable and Lightweight Machine Learning Models for Image Super-Resolution and Denoising.” Her Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Jernej Barbic (Outside Member). The Committee members were pleased with Wei’s thesis work. Here is a summary of her thesis:Image signal processors convert camera sensor data into images via digital image processing operations. In recent years, we have witnessed an increased interest in using Deep Learning to enhance the ISP raw images for improved quality. The main challenge is the regularization of this ill-posed problem.

Practically, the trade-off between quality improvement and the computational complexity is the key issue of widely applying the existing state-of-the-art methods. In this dissertation defense talk, we propose effective and efficient techniques for the typical low-level image enhancement problem, image super-resolution (SR) based Green Learning. First, we propose LSR and LSR++, a light-weight super-resolution green learning method with feedforward design without backpropagation, and its advanced version with significantly smaller computational complexity. Second, we develop Green U-Shaped Learning (GUSL) method for super-resolution with multi-level coarse-to-fine semantic estimation and effective conditional residual prediction. Third, we introduce Green U-Shaped Learning method to medical computed tomography (CT) images for low-dose CT image denoising enhancement to demonstrate its generalization capability. All above, these methods contribute to advanced efficiency with competitiveperformance, mathematical transparency, and robust generalizability in low-level computer vision problems.

Wei generously shared her experiences in the MCL Lab with us: It has been a long journey. First and foremost, I would like to extend my sincere thanks to my Ph.D. advisor, Professor C.-C. Jay Kuo, for his outstanding mentorship and consistent support throughout my doctoral journey. His dedication to research, strong work ethic, and sense [...]

Automatic segmentation of the prostate is a crucial step in the computer-aideddiagnosis of prostate cancer and in treatment planning. Current methods for prostatesegmentation primarily rely on deep learning models with neural networks. However,these models tend to be large and lack transparency, which is essential forphysicians. We proposed a new data-driven 3D prostate segmentation method onMRI named Green U-shaped Learning (GUSL). Different from deep learning basedmethods, GUSL employs a feed-forward system that utilizes successive subspacelearning (SSL).To keep enough detailed information on a dataset with a large image size, wepropose a cascading model in two stages, as shown in Figure 1: (1) segmentation ondownsampled images and (2) segmentation on the cropped patches. GUSL consistsof three main modules, as shown in Figure 2: representation learning, featurelearning, and residual correction. All modules are applied at multiple levels withvarying resolutions. We achieve fine-to-coarse unsupervised representation learning

using cascaded VoxelHop units, as well as coarse-to-fine segmentation throughfeature learning and residual correction. GUSL maintains a very competitive standingperformance-wise with other DL baseline models and keeps a smaller model sizeand less complexity, with transparency for doctors.

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.