17 citations · 24 across the 18 of their papers we have counts for
24 papers
MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRI
Zhengyi Lu, Ming Lu, Chongyu Qu +11
Metal implants in MRI cause severe artifacts that degrade image quality and hinder clinical diagnosis. Traditional approaches address metal artifact reduction (MAR) and accelerated…
SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning
Junchao Zhu, Ruining Deng, Junlin Guo +13
Spatial transcriptomics (ST) is an emerging technology that enables researchers to investigate the molecular relationships underlying tissue morphology. However, acquiring ST data…
From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology
Zhenhao Guo, Rachit Saluja, Tianyuan Yao +13
Fine-grained glomerular subtyping is central to kidney biopsy interpretation, but clinically valuable labels are scarce and difficult to obtain. Existing computational pathology ap…
Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification
Zhenhao Guo, Rachit Saluja, Tianyuan Yao +8
Vision-language models (VLMs) have shown considerable potential in digital pathology, yet their effectiveness remains limited for fine-grained, disease-specific classification task…
Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model
Xueyuan Li, Can Cui, Ruining Deng +7
Purpose: Recent developments in computational pathology have been driven by advances in Vision Foundation Models, particularly the Segment Anything Model (SAM). This model facilita…
Img2ST-Net: Efficient High-Resolution Spatial Omics Prediction from Whole Slide Histology Images via Fully Convolutional Image-to-Image Learning
Junchao Zhu, Ruining Deng, Junlin Guo +10
Recent advances in multi-modal AI have demonstrated promising potential for generating the currently expensive spatial transcriptomics (ST) data directly from routine histology ima…