output
20172026
most citedLow Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

1.7k citations

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12 papers · 1 filter

cs.CV2025★ 1 cited

Self-Paced Learning for Images of Antinuclear Antibodies

Yiyang Jiang, Guangwu Qian, Jiaxin Wu +4

Antinuclear antibody (ANA) testing is a crucial method for diagnosing autoimmune disorders, including lupus, Sjögren's syndrome, and scleroderma. Despite its importance, manual ANA…

cs.CV2025★ 1 cited

GEMeX-RMCoT: An Enhanced Med-VQA Dataset for Region-Aware Multimodal Chain-of-Thought Reasoning

Bo Liu, Xiangyu Zhao, Along He +3

Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images. While recent advances in…

cs.CV2023★ 54 cited

Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification

Pei Liu, Luping Ji, Xinyu Zhang +1

Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classificatio…

cs.CV2021★ 2 cited

SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation

Hong-Yu Zhou, Chengdi Wang, Haofeng Li +4

Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised met…

cs.CV2021★ 102 cited

SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

Xiangde Luo, Tao Song, Guotai Wang +5

Lung nodule detection from 3D Computed Tomography scans plays a vital role in efficient lung cancer screening. Despite the SOTA performance obtained by recent anchor-based detector…

cs.CV2021★ 10 cited

SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering

Bo Liu, Li-Ming Zhan, Li Xu +3

Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and…

cs.CV2020★ 24 cited

Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency

Xiangde Luo, Wenjun Liao, Jieneng Chen +6

Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional Neural Networks (CNN) hav…

cs.CV2020★ 25 cited

Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia

Xi Ouyang, Jiayu Huo, Liming Xia +15

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 20…

cs.CV2020★ 69 cited

SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping

Jiaxiong Qiu, Cai Chen, Shuaicheng Liu +1

The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources. In this work, we design a n…

cs.CV2020

FoCL: Feature-Oriented Continual Learning for Generative Models

Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei +3

In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL). Unlike previous works that aim to solve the c…

cs.CV2018★ 411 cited

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

Hongming Shan, Yi Zhang, Qingsong Yang +5

Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction…

cs.CV2017★ 1.7k cited

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

Qingsong Yang, Pingkun Yan, Yanbo Zhang +5

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein d…