3 citations · 9 across the 13 of their papers we have counts for
14 papers
Optical microscopy for pathological imaging: past, present, and future
Runchen Zhang, Xiaoyi Shang, Liangyu Deng +13
Histopathology, the microscopic examination of stained tissue sections, remains the gold standard for pathology imaging, but its bright-field imaging is largely morphological. Adva…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
Glance and Focus Reinforcement for Pan-cancer Screening
Linshan Wu, Jiaxin Zhuang, Hao Chen
Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT vo…
UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
Linshan Wu, Yuxiang Nie, Sunan He +12
The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. Howev…
Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging
Xiaoyu Zheng, Jing Wen, Jiaxin Zhuang +6
Polarization, as a new optical imaging tool, has been explored to assist in the diagnosis of pathology. Moreover, converting the polarimetric Mueller Matrix (MM) to standardized st…
Beyond H&E: Unlocking Pathological Insights with Polarization Imaging
Yao Du, Jiaxin Zhuang, Xiaoyu Zheng +5
Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H…
FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition
Linshan Wu, Jiaxin Zhuang, Yanning Zhou +12
Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilit…
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training
Xuefeng Ni, Linshan Wu, Jiaxin Zhuang +6
3D medical image analysis is pivotal in numerous clinical applications. However, the scarcity of labeled data and limited generalization capabilities hinder the advancement of AI-e…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Large-Scale 3D Medical Image Pre-training with Geometric Context Priors
Linshan Wu, Jiaxin Zhuang, Hao Chen
The scarcity of annotations poses a significant challenge in medical image analysis. Large-scale pre-training has emerged as a promising label-efficient solution, owing to the util…
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis
Linshan Wu, Jiaxin Zhuang, Xuefeng Ni +1
AI-driven tumor analysis has garnered increasing attention in healthcare. However, its progress is significantly hindered by the lack of annotated tumor cases, which requires radio…
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
Jiaxin Zhuang, Linshan Wu, Qiong Wang +4
The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre-train…
VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis
Linshan Wu, Jiaxin Zhuang, Hao Chen
Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However, the lack of high-level semantics in pre-training still heavily hinders the…
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
Jiaxin Zhuang, Luyang Luo, Zhixuan Chen +1
Deep-learning (DL) based methods are playing an important role in the task of abdominal organs and tumors segmentation in CT scans. However, the large requirements of annotated dat…