Tianyu Zhang

Tianyu Zhang

Artificial Intelligence researcher with extensive experience

Publications

Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI

Published in Medical Image Analysis, 2024

In this study, not only do we propose arbitrary 3D/4D sequence generation within one model to generate any specified target sequence, but also we are able to rank the importance of each sequence based on a new metric estimating the difficulty of a sequence being generated. Furthermore, we also exploit the generation inability of the model to extract regions that contain unique information for each sequence.

Recommended citation: Han, L., Tan, T., Zhang, T. et al. (2023). "Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI." Medical Image Analysis. 92. https://doi.org/10.1016/j.media.2023.103044

An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

Published in MICCAI, 2023

We propose an explainable task-specific synthesis network, which adapts weights automatically for specific sequence generation tasks and provides interpretability and reliability from two sides: (1) visualize and quantify the contribution of each input sequence in the fusion stage by a trainable task-specific weighted average module; (2) highlight the area the network tried to refine during synthesizing by a task-specific attention module.

Recommended citation: Han, L., Zhang, T., Hunag, Y. et al. (2023). "An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis." MICCAI. https://doi.org/10.1007/978-3-031-43999-5_5

DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-adversarial Learning

Published in MICCAI, 2023

We propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG.

Recommended citation: Wang, X., Tan, T., Gao, Y. et al. (2023). "DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-adversarial Learning." MICCAI. https://doi.org/10.1007/978-3-031-43990-2_6

Synthesis of Contrast-Enhanced Breast MRI Using T1- and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention Mechanism

Published in MICCAI, 2023

In this study, we develop a multi-sequence fusion network to synthesize CE-MRI based on T1-weighted MRI and DWIs.

Recommended citation: Zhang, T., Han, L., D‘Angelo, A. et al. (2023). "Synthesis of Contrast-Enhanced Breast MRI Using T1-and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention Mechanism." MICCAI. https://doi.org/10.1007/978-3-031-43990-2_8

RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease

Published in Cell Reports Medicine, 2023

Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning.

Recommended citation: Zhang, T., Tan, T., Wang, X. et al. (2023). "RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease." Cell Reports Medicine. 4(8). https://doi.org/10.1016/j.xcrm.2023.101131

Radiomics and artificial intelligence in breast imaging: a survey

Published in Artificial Intelligence Review, 2023

In this comprehensive review, we cover the progress, application and challenge of radiomics and AI in breast cancer diagnosis in recent years, as well as the impact and significance of AI on future breast cancer research.

Recommended citation: Zhang, T., Tan, T., Samperna, R. et al. (2023). "Radiomics and artificial intelligence in breast imaging: a survey." Artificial Intelligence Review. 56(1). https://doi.org/10.1007/s10462-023-10543-y

Predicting breast cancer types on and beyond molecular level in a multi-modal fashion

Published in NPJ breast cancer, 2023

Accurately determining the molecular subtypes of breast cancer is important for the prognosis of breast cancer patients and can guide treatment selection. In this study, we develop a deep learning-based model for predicting the molecular subtypes of breast cancer directly from the diagnostic mammography and ultrasound images.

Recommended citation: Zhang, T., Tan, T., Han, L. et al. (2023). "Predicting breast cancer types on and beyond molecular level in a multi-modal fashion." NPJ breast cancer. 9(16). https://doi.org/10.1038/s41523-023-00517-2

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts

Published in Insights into Imaging, 2023

To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts.

Recommended citation: Tan, T., Rodriguez-Ruiz, A., Zhang, T. et al. (2023). "Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts." Insights into Imaging. 14(10). https://doi.org/10.1186/s13244-022-01352-y

From community acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

Published in European Radiology, 2020

In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images.

Recommended citation: Li, Z., Zhong, Z., Li, Y., Zhang, T. et al. (2020). "From community acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans." European Radiology. 30(1). https://doi.org/10.1007/s00330-020-07042-x