108 citations · 120 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
FCNR: Fast Compressive Neural Representation of Visualization Images
Yunfei Lu, Pengfei Gu, Chaoli Wang
We present FCNR, a fast compressive neural representation for tens of thousands of visualization images under varying viewpoints and timesteps. The existing NeRVI solution, albeit…
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…
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
Hongxiao Wang, Yang Yang, Zhuo Zhao +3
For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, a…