452 citations · 1.1k across the 46 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
Riqiang Gao, Thomas Li, Yucheng Tang +7
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains inc…
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Jun Li, Junyu Chen, Yucheng Tang +3
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance…
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Yucheng Tang, Dong Yang, Wenqi Li +5
Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications. Inspired…
Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT
Riqiang Gao, Mirza S. Khan, Yucheng Tang +6
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-…
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
Ho Hin Lee, Yucheng Tang, Qi Yang +6
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask. To increase the ability of encoder-…
Outlier Guided Optimization of Abdominal Segmentation
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection
Yiyuan Yang, Riqiang Gao, Yucheng Tang +6
Recently, multi-task networks have shown to both offer additional estimation capabilities, and, perhaps more importantly, increased performance over single-task networks on a "main…
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Riqiang Gao, Lingfeng Li, Yucheng Tang +6
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e.g., heavy smokers between 55 and 80 years old). The reco…
More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring
Haofu Liao, Yucheng Tang, Gareth Funka-Lea +2
Using catheter ablation to treat atrial fibrillation increasingly relies on intracardiac echocardiography (ICE) for an anatomical delineation of the left atrium and the pulmonary v…