3 citations · 8 across the 14 of their papers we have counts for
15 papers
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation
Jiaqi Tang, Yu Wang, Yang Liu +7
Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-tuning protocols. Models als…
SAMI3D-DW: Interactive Segmentation of Any 3D Medical Images
Ping Gong, Shiyuan Su, Fandong Zhang +4
Interactive segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and refine their targets. Despite…
Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models
Jiaqi Tang, Shaoyang Zhang, Fandong Zhang +3
The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, whic…
The Texture-Shape Dilemma: Boundary-Safe Synthetic Generation for 3D Medical Transformers
Jiaqi Tang, Weixuan Xu, Shu Zhang +2
Vision Transformers (ViTs) have revolutionized medical image analysis, yet their data-hungry nature clashes with the scarcity and privacy constraints of clinical archives. Formula-…
Fake It Right: Injecting Anatomical Logic into Synthetic Supervised Pre-training for Medical Segmentation
Jiaqi Tang, Mengyan Zheng, Shu Zhang +2
Vision Transformers (ViTs) excel in 3D medical segmentation but require massive annotated datasets. While Self-Supervised Learning (SSL) mitigates this using unlabeled data, it sti…
LDRNet: Large Deformation Registration Model for Chest CT Registration
Cheng Wang, Qiyu Gao, Fandong Zhang +2
Most of the deep learning based medical image registration algorithms focus on brain image registration tasks.Compared with brain registration, the chest CT registration has larger…
Autoregressive Sequence Modeling for 3D Medical Image Representation
Siwen Wang, Churan Wang, Fei Gao +4
Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse…
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
Fei Gao, Siwen Wang, Fandong Zhang +5
Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SS…
Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
Gangming Zhao, Kongming Liang, Chengwei Pan +4
Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use st…
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification
Churan Wang, Jing Li, Xinwei Sun +3
Due to its safety-critical property, the image-based diagnosis is desired to achieve robustness on out-of-distribution (OOD) samples. A natural way towards this goal is capturing o…
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition
Mingzhou Liu, Xinwei Sun, Fandong Zhang +2
Context, as referred to situational factors related to the object of interest, can help infer the object's states or properties in visual recognition. As such contextual features a…
Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection
Yuhang Liu, Fandong Zhang, Chaoqi Chen +3
Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable…
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
Chu-ran Wang, Jing Li, Fandong Zhang +4
Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions…
Integrating Feature and Image Pyramid: A Lung Nodule Detector Learned in Curriculum Fashion
Benyuan Sun, Zhen Zhou, Fandong Zhang +2
Lung nodules suffer large variation in size and appearance in CT images. Nodules less than 10mm can easily lose information after down-sampling in convolutional neural networks, wh…
FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis
Xinwei Sun, Lingjing Hu, Fandong Zhang +2
Recent studies found that in voxel-based neuroimage analysis, detecting and differentiating "procedural bias" that are introduced during the preprocessing steps from lesion feature…