280 citations · 389 across the 14 of their papers we have counts for
14 papers
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
Lin Tian, Zi Li, Fengze Liu +7
Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locatio…
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Yuyin Zhou, Xianhang Li, Fengze Liu +6
Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data…
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data
Yuyin Zhou, David Dreizin, Yan Wang +3
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular in…
Intriguing Findings of Frequency Selection for Image Deblurring
Xintian Mao, Yiming Liu, Fengze Liu +3
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always desig…
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
Fengze Liu, Ke Yan, Adam Harrison +8
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of compu…
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Yingda Xia, Dong Yang, Zhiding Yu +7
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely ex…
JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
Fengze Liu, Jinzheng Cai, Yuankai Huo +8
Multi-modal image registration is a challenging problem that is also an important clinical task for many real applications and scenarios. As a first step in analysis, deformable re…
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
Yingda Xia, Yi Zhang, Fengze Liu +2
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of…
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Yan Wang, Xu Wei, Fengze Liu +5
Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related d…
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans
Fengze Liu, Yuyin Zhou, Elliot Fishman +1
Automatic abnormality detection in abdominal CT scans can help doctors improve the accuracy and efficiency in diagnosis. In this paper we aim at detecting pancreatic ductal adenoca…
An Alarm System For Segmentation Algorithm Based On Shape Model
Fengze Liu, Yingda Xia, Dong Yang +2
It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile…
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Yingda Xia, Fengze Liu, Dong Yang +6
While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applicati…
Joint Shape Representation and Classification for Detecting PDAC
Fengze Liu, Lingxi Xie, Yingda Xia +2
We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task w…
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
Yingda Xia, Lingxi Xie, Fengze Liu +3
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this pa…