2 citations · 2 across the 10 of their papers we have counts for
10 papers
TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging
Guangyu Meng, Pengfei Gu, Xueyang Li +3
Topological data analysis (TDA), particularly persistent homology (PH), captures geometric structural properties in medical images (e.g., connected components, loops, shape charact…
TopoCL: Topological Contrastive Learning for Medical Imaging
Guangyu Meng, Pengfei Gu, Peixian Liang +3
Contrastive learning (CL) has become a powerful approach for learning representations from unlabeled images. However, existing CL methods focus predominantly on visual appearance f…
Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification
Pengfei Gu, Huimin Li, Haoteng Tang +6
Modern deep neural networks have shown remarkable performance in medical image classification. However, such networks either emphasize pixel-intensity features instead of fundament…
TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classification
Pengfei Gu, Hongxiao Wang, Yejia Zhang +3
Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). % Despite remarkable su…
Adapting a Segmentation Foundation Model for Medical Image Classification
Pengfei Gu, Haoteng Tang, Islam A. Ebeid +7
Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to t…
Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network
Delin An, Pengfei Gu, Milan Sonka +2
Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large…
Boosting Medical Image Classification with Segmentation Foundation Model
Pengfei Gu, Zihan Zhao, Hongxiao Wang +5
The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images. Recently, SAM has gained a great deal of attention for its applicati…
Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation
Pengfei Gu, Huimin Li, Yejia Zhang +2
Masked Autoencoders (MAEs) have been shown to be effective in pre-training Vision Transformers (ViTs) for natural and medical image analysis problems. By reconstructing missing pix…
Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation
Suraj Mishra, Danny Z. Chen
Medical image segmentation using deep neural networks has been highly successful. However, the effectiveness of these networks is often limited by inadequate dense prediction and i…
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Yizhe Zhang, Shuo Wang, Tao Zhou +2
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates…