276 citations · 714 across the 20 of their papers we have counts for
10 papers · 1 filter
SSDL: Self-Supervised Dictionary Learning
Shuai Shao, Lei Xing, Wei Yu +3
The label-embedded dictionary learning (DL) algorithms generate influential dictionaries by introducing discriminative information. However, there exists a limitation: All the labe…
MDFM: Multi-Decision Fusing Model for Few-Shot Learning
Shuai Shao, Lei Xing, Rui Xu +3
In recent years, researchers pay growing attention to the few-shot learning (FSL) task to address the data-scarce problem. A standard FSL framework is composed of two components: i…
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries +8
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning arch…
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Yang Song, Liyue Shen, Lei Xing +1
Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based o…
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
Lequan Yu, Zhicheng Zhang, Xiaomeng Li +3
The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiatio…
CIM: Class-Irrelevant Mapping for Few-Shot Classification
Shuai Shao, Lei Xing, Yixin Chen +3
Few-shot classification (FSC) is one of the most concerned hot issues in recent years. The general setting consists of two phases: (1) Pre-train a feature extraction model (FEM) wi…
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
Shuai Shao, Lei Xing, Yan Wang +4
Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL framework is composed of two components: (1) Pre-train. Employ the base data to generate a CNN-based…
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
Liyue Shen, John Pauly, Lei Xing
Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses addition challenge…
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Liyue Shen, Wei Zhao, Dante Capaldi +2
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws. Fundamentally, most deep…
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
Junfei Xiao, Lequan Yu, Zongwei Zhou +4
Batch normalization (BN) uniformly shifts and scales the activations based on the statistics of a batch of images. However, the intensity distribution of the background pixels ofte…