HistDiT introduces a structure-aware latent conditional DiT with dual-stream conditioning and multi-objective loss that outperforms GANs and U-Net diffusion models for high-fidelity virtual histological staining.
In: ICLR (2021)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GLASSNet outperforms prior methods on salient object detection benchmarks by freezing SAMv2, adding a spatially aware adapter, and fusing outputs from global and local decoders.
citing papers explorer
-
HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology
HistDiT introduces a structure-aware latent conditional DiT with dual-stream conditioning and multi-objective loss that outperforms GANs and U-Net diffusion models for high-fidelity virtual histological staining.
-
Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection
GLASSNet outperforms prior methods on salient object detection benchmarks by freezing SAMv2, adding a spatially aware adapter, and fusing outputs from global and local decoders.