139 citations · 499 across the 25 of their papers we have counts for
25 papers
Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation
Zilin Lu, Ruifeng Yuan, Weiwei Cao +6
Radiologists highly desire fully automated AI for radiology report generation (R2G), yet existing approaches fall short in clinical utility. Reinforcement learning (RL) holds poten…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model
Yiwen Ye, Ziyang Chen, Jianpeng Zhang +2
Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities e…
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
Yiwen Ye, Yutong Xie, Jianpeng Zhang +3
Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consum…
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
Jianpeng Zhang, Xianghua Ye, Jianfeng Zhang +8
Lung cancer is a leading cause of death worldwide and early screening is critical for improving survival outcomes. In clinical practice, the contextual structure of nodules and the…
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Nicholas Heller, Fabian Isensee, Dasha Trofimova +42
This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical…
Attention Mechanisms in Medical Image Segmentation: A Survey
Yutong Xie, Bing Yang, Qingbiao Guan +3
Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in…
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
Yiwen Ye, Yutong Xie, Jianpeng Zhang +2
The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build univer…
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Jieneng Chen, Yingda Xia, Jiawen Yao +22
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus o…
Learning from partially labeled data for multi-organ and tumor segmentation
Yutong Xie, Jianpeng Zhang, Yong Xia +1
Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream app…
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers
Yutong Xie, Jianpeng Zhang, Yong Xia +2
Although Transformers have successfully transitioned from their language modelling origins to image-based applications, their quadratic computational complexity remains a challenge…
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Hu Wang, Jianpeng Zhang, Yuanhong Chen +4
Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process. However, this equal combination can be detrimenta…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier
Yutong Xie, Jianpeng Zhang, Yong Xia +1
Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D me…
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation
Shishuai Hu, Zehui Liao, Jianpeng Zhang +1
The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model…
Kernel Adversarial Learning for Real-world Image Super-resolution
Hu Wang, Congbo Ma, Jianpeng Zhang +2
Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and addit…
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
Yutong Xie, Jianpeng Zhang, Chunhua Shen +1
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. The convolutional operations used in these networks, however, inevi…
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
Jianpeng Zhang, Yutong Xie, Yan Wang +1
Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules.…
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
Yutong Xie, Jianpeng Zhang, Zehui Liao +2
It has been widely recognized that the success of deep learning in image segmentation relies overwhelmingly on a myriad amount of densely annotated training data, which, however, a…
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Jianpeng Zhang, Yutong Xie, Yong Xia +1
Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of org…
Pairwise Relation Learning for Semi-supervised Gland Segmentation
Yutong Xie, Jianpeng Zhang, Zhibin Liao +3
Accurate and automated gland segmentation on histology tissue images is an essential but challenging task in the computer-aided diagnosis of adenocarcinoma. Despite their prevalenc…
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
Jianpeng Zhang, Yutong Xie, Guansong Pang +8
Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detecti…
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
Yutong Xie, Jianpeng Zhang, Yong Xia +1
Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learni…
A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images
Yutong Xie, Jianpeng Zhang, Yong Xia
A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images. In this model, four pre-trai…