452 citations · 1.1k across the 46 of their papers we have counts for
50 papers
NV-Generate-Body: 3D Latent Diffusion Model forControllable Anatomy, Tumor, and Demographic Body Mask Synthesis
Can Zhao, Pengfei Guo, Dong Yang +8
Curating large labeled CT datasets is expensive and constrained by patient privacy. We present NV-Generate-BodyMask, a 3D latent diffusion model that synthesizes whole-body CT segm…
Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI
Yexiao He, Yucheng Tang, Pengfei Guo +6
Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve. This is especially concerning in…
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
Ruining Deng, Tianyuan Yao, Yucheng Tang +44
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard f…
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…
Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation
Xin Yu, Qi Yang, Han Liu +10
2D single-slice abdominal computed tomography (CT) enables the assessment of body habitus and organ health with low radiation exposure. However, single-slice data necessitates the…
HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
Ruining Deng, Quan Liu, Can Cui +10
Panoramic image segmentation in computational pathology presents a remarkable challenge due to the morphologically complex and variably scaled anatomy. For instance, the intricate…
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
Ruining Deng, Quan Liu, Can Cui +11
Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various…
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
Ho Hin Lee, Adam M. Saunders, Michael E. Kim +15
Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing…
Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization
Xin Yu, Qi Yang, Yucheng Tang +8
Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between…
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
Xin Yu, Yucheng Tang, Qi Yang +4
Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa…
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…
High-performance Data Management for Whole Slide Image Analysis in Digital Pathology
Haoju Leng, Ruining Deng, Shunxing Bao +8
When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deplo…
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
Can Cui, Yaohong Wang, Shunxing Bao +11
Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during train…
Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning
Xueyuan Li, Ruining Deng, Yucheng Tang +3
Precise identification of multiple cell classes in high-resolution Giga-pixel whole slide imaging (WSI) is critical for various clinical scenarios. Building an AI model for this pu…
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang +8
Harnessing the power of pre-training on large-scale datasets like ImageNet forms a fundamental building block for the progress of representation learning-driven solutions in comput…
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
Ruining Deng, Yanwei Li, Peize Li +11
Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires lab…
Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
Yinchi Zhou, Ho Hin Lee, Yucheng Tang +6
With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in disti…
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
Chongyu Qu, Tiezheng Zhang, Hualin Qiao +4
Annotating medical images, particularly for organ segmentation, is laborious and time-consuming. For example, annotating an abdominal organ requires an estimated rate of 30-60 minu…
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging
Ruining Deng, Can Cui, Quan Liu +13
The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed an…
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen +7
An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
Ho Hin Lee, Yucheng Tang, Han Liu +7
Recent studies have demonstrated the superior performance of introducing ``scan-wise" contrast labels into contrastive learning for multi-organ segmentation on multi-phase computed…
Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models
Xin Yu, Qi Yang, Yucheng Tang +8
2D low-dose single-slice abdominal computed tomography (CT) slice enables direct measurements of body composition, which are critical to quantitatively characterizing health relati…
Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation
Xin Yu, Yucheng Tang, Qi Yang +6
Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitati…