97 citations · 140 across the 35 of their papers we have counts for
19 papers · 1 filter
From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology
Zhenhao Guo, Rachit Saluja, Tianyuan Yao +14
Fine-grained glomerular subtyping is essential for kidney biopsy interpretation, yet it presents a harder setting than the broad organ- or disease-level recognition tasks commonly…
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…
Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization
Ho Hin Lee, Quan Liu, Shunxing Bao +2
Large kernel convolutions offer a scalable alternative to vision transformers for high-resolution 3D volumetric analysis, yet naively increasing kernel size often leads to optimiza…
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…
GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation
Jialin Yue, Tianyuan Yao, Ruining Deng +7
Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli, the key functional units of the kidney, from whole slide images (WSIs) i…
Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment
Junlin Guo, Siqi Lu, Can Cui +14
Training AI foundation models has emerged as a promising large-scale learning approach for addressing real-world healthcare challenges, including digital pathology. While many of t…
PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models
Can Cui, Ruining Deng, Junlin Guo +4
The Vision Foundation Model has recently gained attention in medical image analysis. Its zero-shot learning capabilities accelerate AI deployment and enhance the generalizability o…
Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results
Jialin Yue, Tianyuan Yao, Ruining Deng +5
Recently, the use of circle representation has emerged as a method to improve the identification of spherical objects (such as glomeruli, cells, and nuclei) in medical imaging stud…
Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction
Quan Liu, Jiawen Yao, Lisha Yao +6
Colorectal cancer (CRC) micro-satellite instability (MSI) prediction on histopathology images is a challenging weakly supervised learning task that involves multi-instance learning…
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution
Ho Hin Lee, Quan Liu, Qi Yang +4
The application of 3D ViTs to medical image segmentation has seen remarkable strides, somewhat overshadowing the budding advancements in Convolutional Neural Network (CNN)-based mo…
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning
Can Cui, Ruining Deng, Quan Liu +5
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach. With the zero-shot segmentation capacity, SAM…
Digital Modeling on Large Kernel Metamaterial Neural Network
Quan Liu, Hanyu Zheng, Brandon T. Swartz +5
Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, sign…
Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
Hanyu Zheng, Quan Liu, Ivan I. Kravchenko +3
Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically nece…
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning
Tianyuan Yao, Chang Qu, Jun Long +13
With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more gene…
Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes
Jiachen Xu, Junlin Guo, James Zimmer-Dauphinee +7
Archaeology has long faced fundamental issues of sampling and scalar representation. Traditionally, the local-to-regional-scale views of settlement patterns are produced through sy…
Compound Figure Separation of Biomedical Images with Side Loss
Tianyuan Yao, Chang Qu, Quan Liu +11
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large…
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Mengyang Zhao, Quan Liu, Aadarsh Jha +6
Recent advances in bioimaging have provided scientists a superior high spatial-temporal resolution to observe dynamics of living cells as 3D volumetric videos. Unfortunately, the 3…
CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns
Ruining Deng, Quan Liu, Shunxing Bao +5
Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biome…