2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images
Hamid Manoochehri, Bodong Zhang, Beatrice S. Knudsen +1
Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learni…
VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models
Harshit, Tolga Tasdizen
The recent developments in deep learning led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated Vision and Language Mode…
DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention
Xiaoya Tang, Bodong Zhang, Beatrice S. Knudsen +1
We here propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced represe…
DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading
Man M. Ho, Elham Ghelichkhan, Yosep Chong +3
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
Man M. Ho, Shikha Dubey, Yosep Chong +2
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 decis…