5 papers · 1 filter
Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting
Wen Zhang, Qin Ren, Wenjing Liu +2
Accurate nuclear instance segmentation is a pivotal task in computational pathology, supporting data-driven clinical insights and facilitating downstream translational applications…
Scale Where It Matters: Training-Free Localized Scaling for Diffusion Models
Qin Ren, Yufei Wang, Lanqing Guo +3
Diffusion models have become the dominant paradigm in text-to-image generation, and test-time scaling (TTS) improves sample quality by allocating additional computation at inferenc…
Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis
Wenjing Liu, Qin Ren, Wen Zhang +2
Multimodal survival analysis aims to improve cancer prognosis using heterogeneous biomedical data, such as histopathology images and genomic profiles. A common strategy is to align…
Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering
Shanlin Sun, Yifan Wang, Hanwen Zhang +5
While multi-step diffusion models have advanced both forward and inverse rendering, existing approaches often treat these problems independently, leading to cycle inconsistency and…
OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport
Qin Ren, Yifan Wang, Ruogu Fang +2
Survival prediction using whole slide images (WSIs) can be formulated as a multiple instance learning (MIL) problem. However, existing MIL methods often fail to explicitly capture…