Search Results for author:
Found 7 papers, 3 papers with code
Vulnerabilities Unveiled: Adversarially Attacking a Multimodal Vision Language Model for Pathology Imaging
In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted adversarial conditions.
Predicting Future States with Spatial Point Processes in Single Molecule Resolution Spatial Transcriptomics
In this paper, we introduce a pipeline based on Random Forest Regression to predict the future distribution of cells that are expressed by the Sog-D gene (active cells) in both the Anterior to posterior (AP) and the Dorsal to Ventral (DV) axis of the Drosophila in embryogenesis process.
Histopathology Slide Indexing and Search: Are We There Yet?
The search and retrieval of digital histopathology slides is an important task that has yet to be solved.
The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility.
Generalizability of PRS313 for breast cancer risk amongst non-Europeans in a Los Angeles biobank
We found that the PRS313 achieved overlapping Areas under the ROC Curve (AUCs) in females of Lantix (AUC, 0. 68; 95 CI, 0. 65-0. 71) and European ancestry (AUC, 0. 70; 95 CI, 0. 69-0. 71) but lower AUCs for the AFR and EAA populations (AFR: AUC, 0. 61; 95 CI, 0. 56-0. 65; EAA: AUC, 0. 64; 95 CI, 0. 60-0. 680).
Clinically Relevant Latent Space Embedding of Cancer Histopathology Slides through Variational Autoencoder Based Image Compression
We generate and visualize embeddings from the compressed latent space and demonstrate how they are useful for clinical interpretation of data, and how in the future such latent embeddings can be used to accelerate search of clinical imaging data.
A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels
We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set.
