44 citations · 115 across the 15 of their papers we have counts for
15 papers
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
Yuanbin Chen, Tao Wang, Hui Tang +6
While supervised learning has achieved remarkable success, obtaining large-scale labeled datasets in biomedical imaging is often impractical due to high costs and the time-consumin…
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
Xin Wang, Tao Tan, Yuan Gao +6
Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what…
Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism
Tianyu Zhang, Luyi Han, Anna D'Angelo +7
Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides supe…
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
Tao Wang, Xinlin Zhang, Yuanbo Zhou +5
In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides stat…
IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data
Tianyu Zhang, Tao Tan, Luyi Han +5
Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of les…
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Luyi Han, Tao Tan, Tianyu Zhang +5
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across…
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning
Luyi Han, Yunzhi Huang, Tao Tan +1
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed…
Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net
Hamed Fayyaz, Ehsan Kozegar, Tao Tan +1
Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection. Whil…
Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19
Tao Tan, Bipul Das, Ravi Soni +13
The COVID-19 pandemic continues to spread and impact the well-being of the global population. The front-line modalities including computed tomography (CT) and X-ray play an importa…
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
Zhang Li, Jiehua Zhang, Tao Tan +30
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in…
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Xin Wang, Ruisheng Su, Weiyi Xie +5
In medical-data driven learning, 3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks, proving the added val…
Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets
Jie Xing, Zheren Li, Biyuan Wang +6
Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the b…
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Zhang Li, Zheyu Hu, Jiaolong Xu +10
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform be…
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
Tao Tan, Zhang Li, Haixia Liu +15
Bronchoscopy inspection as a follow-up procedure from the radiological imaging plays a key role in lung disease diagnosis and determining treatment plans for the patients. Doctors…
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network
Dongsheng Jiang, Weiqiang Dou, Luc Vosters +3
The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retriev…