Search Results for author:
Found 3 papers, 1 papers with code
DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading
Latent Diffusion Models (LDMs) can generate high-fidelity images from noise, offering a promising approach for augmenting histopathology images for training cancer grading models.
F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation
The Frozen Section (FS) technique is a rapid and efficient method, taking only 15-30 minutes to prepare slides for pathologists' evaluation during surgery, enabling immediate decisions on further surgical interventions.
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
On the other hand, acquiring extensive datasets with localized labels for training is not feasible.
Contrastive Learning
Histopathological Image Classification
+2
