1.1k citations · 2k across the 46 of their papers we have counts for
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CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
Fenghe Tang, Jianrui Ding, Lingtao Wang +2
The U-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a f…
Unsupervised augmentation optimization for few-shot medical image segmentation
Quan Quan, Shang Zhao, Qingsong Yao +2
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples. Howev…
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Qing Wu, Lixuan Chen, Ce Wang +4
Emerging neural reconstruction techniques based on tomography (e.g., NeRF, NeAT, and NeRP) have started showing unique capabilities in medical imaging. In this work, we present a n…
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
Ziyuan Zhao, Fangcheng Zhou, Zeng Zeng +2
Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achi…
Unified Multi-Modal Image Synthesis for Missing Modality Imputation
Yue Zhang, Chengtao Peng, Qiuli Wang +3
Multi-modal medical images provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption a…
DiffULD: Diffusive Universal Lesion Detection
Peiang Zhao, Han Li, Ruiyang Jin +1
Universal Lesion Detection (ULD) in computed tomography (CT) plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by anchor-based detection…
Causal Image Synthesis of Brain MR in 3D
Yujia Li, Jiong Shi, S. Kevin Zhou
Clinical decision making requires counterfactual reasoning based on a factual medical image and thus necessitates causal image synthesis. To this end, we present a novel method for…
Evidence-aware multi-modal data fusion and its application to total knee replacement prediction
Xinwen Liu, Jing Wang, S. Kevin Zhou +2
Deep neural networks have been widely studied for predicting a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imag…
Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field
Ziqi Gao, S. Kevin Zhou
Undersampled MRI reconstruction is crucial for accelerating clinical scanning. Dual-domain reconstruction network is performant among SoTA deep learning methods. In this paper, we…
FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification
Zikang Xu, Shang Zhao, Quan Quan +2
Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions. Researchers obse…
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Jun Li, Kexin Li, Yafeng Zhou +1
Targeted diagnosis and treatment plans for patients with coronary artery disease vary according to atherosclerotic plaque component. Coronary CT angiography (CCTA) is widely used f…
Distortion-Disentangled Contrastive Learning
Jinfeng Wang, Sifan Song, Jionglong Su +1
Self-supervised learning is well known for its remarkable performance in representation learning and various downstream computer vision tasks. Recently, Positive-pair-Only Contrast…
Mixed-order self-paced curriculum learning for universal lesion detection
Han Li, Hu Han, S. Kevin Zhou
Self-paced curriculum learning (SCL) has demonstrated its great potential in computer vision, natural language processing, etc. During training, it implements easy-to-hard sampling…
Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation
Pengbo Liu, Mengke Sun, S. Kevin Zhou
Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requir…
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min +5
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pa…