1.1k citations · 2k across the 46 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
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…
BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations
Jun Wei, Yiwen Hu, Guanbin Li +3
Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing pol…
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
Heqin Zhu, Xu Sun, Yuexiang Li +3
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…
DATR: Domain-adaptive transformer for multi-domain landmark detection
Heqin Zhu, Qingsong Yao, S. Kevin Zhou
Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based o…
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image
Yuanqi Du, Quan Quan, Hu Han +1
Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e.g., with lesions), is critical in many perspectives, from lesion detection…
Conditional Training with Bounding Map for Universal Lesion Detection
Han Li, Long Chen, Hu Han +1
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by coarse-to-fine two-stage de…
One-Shot Medical Landmark Detection
Qingsong Yao, Quan Quan, Li Xiao +1
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medica…
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models
Quan Quan, Qiyuan Wang, Liu Li +2
While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable med…
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Qingsong Yao, Zecheng He, Yi Lin +3
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making. Luckily, medical A…
Aggregative Self-Supervised Feature Learning from a Limited Sample
Jiuwen Zhu, Yuexiang Li, S. Kevin Zhou
Self-supervised learning (SSL) is an efficient approach that addresses the issue of limited training data and annotation shortage. The key part in SSL is its proxy task that define…
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos +6
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propell…
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning
Jiuwen Zhu, Yuexiang Li, Yifan Hu +1
Deep learning highly relies on the amount of annotated data. However, annotating medical images is extremely laborious and expensive. To this end, self-supervised learning (SSL), a…
First image then video: A two-stage network for spatiotemporal video denoising
Ce Wang, S. Kevin Zhou, Zhiwei Cheng
Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising ten…
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
Hao Chen, Yefeng Zheng, Jin-Hyeong Park +2
Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has b…