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Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models
Runchen Wang, Junlin Guo, Siqi Lu +12
Accurate cell nuclei segmentation is critical for downstream tasks in kidney pathology and remains a major challenge due to the morphological diversity and imaging variability of r…
Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework
Chongyu Qu, Allen J. Luna, Thomas Z. Li +6
Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all co…
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…
ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction
Juming Xiong, Ruining Deng, Jialin Yue +10
Histological analysis plays a crucial role in understanding tissue structure and pathology. While recent advancements in registration methods have improved 2D histological analysis…
DeepAndes: A Self-Supervised Vision Foundation Model for Multi-Spectral Remote Sensing Imagery of the Andes
Junlin Guo, James R. Zimmer-Dauphinee, Jordan M. Nieusma +16
By mapping sites at large scales using remotely sensed data, archaeologists can generate unique insights into long-term demographic trends, inter-regional social networks, and past…
MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics
Junchao Zhu, Ruining Deng, Tianyuan Yao +12
The rapid development of spatial transcriptomics (ST) offers new opportunities to explore the gene expression patterns within the spatial microenvironment. Current research integra…
Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines
Chongyu Qu, Ritchie Zhao, Ye Yu +6
Quantizing deep neural networks ,reducing the precision (bit-width) of their computations, can remarkably decrease memory usage and accelerate processing, making these models more…