10 citations · 11 across the 6 of their papers we have counts for
6 papers
Pathway-Structured Privileged Distillation for Deployable Computational Pathology
Yongxin Guo, Hao Lu, Onur Koyun +2
Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA profiling in routine settings.…
Momentum Memory for Knowledge Distillation in Computational Pathology
Yongxin Guo, Hao Lu, Onur C. Koyun +3
Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer diagnosis, yet its clinical translation is hindered by the limited availability…
PCA-VAE: Differentiable Subspace Quantization without Codebook Collapse
Hao Lu, Onur C. Koyun, Yongxin Guo +3
Vector-quantized autoencoders deliver high-fidelity latents but suffer inherent flaws: the quantizer is non-differentiable, requires straight-through hacks, and is prone to collaps…
Beyond Stationarity: Rethinking Codebook Collapse in Vector Quantization
Hao Lu, Onur C. Koyun, Yongxin Guo +3
Vector Quantization (VQ) underpins many modern generative frameworks such as VQ-VAE, VQ-GAN, and latent diffusion models. Yet, it suffers from the persistent problem of codebook co…
Hyperparameter Optimization and Reproducibility in Deep Learning Model Training
Usman Afzaal, Ziyu Su, Usama Sajjad +4
Reproducibility remains a critical challenge in foundation model training for histopathology, often hindered by software randomness, hardware non-determinism, and inconsistent hype…
Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates
Mostafa Rezapour, Muhammad Khalid Khan Niazi, Hao Lu +2
This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a machine learning-based approach, for analyzing gene expression data obtained from nonhuman pri…