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20182023
most citedLesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets

44 citations · 115 across the 15 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

eess.IV2023★ 3 cited

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…

cs.CV2023★ 2 cited

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…

eess.IV2023★ 2 cited

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…

eess.IV2023

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…

eess.IV2023

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…

cs.CV2023

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…