2 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
Hikmat Khan, Usama Sajjad, Metin N. Gurcan +4
Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treatment stratification, yet exist…
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…