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Found 9 papers, 2 papers with code
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.
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.
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis and tumor recurrence tracking.
Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon
Quantitative metrics such as FID and SSIM are frequently used for the analysis of generative models, but they do not correlate explicitly with higher-quality virtual staining results.
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
In this study, we compare the performance of gland and cell segmentation tasks with histopathology domain-specific and non-domain-specific (real-world images) pretrained weights.
Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching
Nonetheless, they often fail to generalize when there is a significant domain (i. e., distributional) shift between the training (i. e., source) data and the dataset(s) encountered when deployed (i. e., target), necessitating manual annotations for the target data to achieve acceptable performance.
A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology
Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides.
Stain Based Contrastive Co-training for Histopathological Image Analysis
Two separate CNNs are used to embed the two views into a joint feature space.
Visual attention analysis of pathologists examining whole slide images of Prostate cancer
To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist.
