Ms. Xiaohui Zhang Profile

Xiaohui Zhang

SPIE Involvement:

Author

Area of Expertise:

Medical Image Processing , Task-based Evaluation , Medical Image Analysis , Neuroimaging , Deep Learning , Machine Learning

Websites:

Personal Website

Xiaohui Zhang is a Ph.D. candidate with the Department of Bioengineering, University of Illinois at Urbana-Champaign (UIUC). She received the B.E. degree in biomedical engineering from Beihang University, Beijing, China, in 2018. Her research interests include computational methods for neuroimaging and machine learning for medical imaging applications. She is also a student member of the SPIE and RSNA (Radiological Society of North America).

SPIE Journal Paper | 4 April 2024 Open Access

Deep-learning-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics

Xiaohui Zhang, Mimi Tan, Mansour Nabil, Richa Shukla, Shaleen Vasavada, Sharmila Anandasabapathy, Mark Anastasio, Elena Petrova

JBO, Vol. 29, Issue 04, 046001, (April 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.4.046001

KEYWORDS: Optical fibers, Lawrencium, Image quality, Super resolution, Endoscopy, Image restoration, Education and training, Cancer, Image enhancement, Image acquisition

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Proceedings Article | 2 April 2024 Presentation + Paper

Medical image classification using self-supervised learning-based masked autoencoder

Zong Fan, Zhimin Wang, Ping Gong, Christine Lee, Shanshan Tang, Xiaohui Zhang, Yao Hao, Zhongwei Zhang, Pengfei Song, Shigao Chen, Hua Li

Proceedings Volume 12926, 129260G (2024) https://doi.org/10.1117/12.3006938

KEYWORDS: Education and training, Image classification, Data modeling, Medical imaging, Tumors, Performance modeling, Classification systems, Visualization, Image restoration, Visual process modeling

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Proceedings Article | 3 April 2023 Presentation + Paper

An auxiliary attention-based network for joint classification and localization of breast tumor on ultrasound images

Zong Fan, Ping Gong, Shanshan Tang, Christine Lee, Xiaohui Zhang, Zhimin Wang, Pengfei Song, Shigao Chen, Hua Li

Proceedings Volume 12464, 1246406 (2023) https://doi.org/10.1117/12.2654106

KEYWORDS: Education and training, Image classification, Tumors, Ultrasonography, Breast, Feature extraction, Network architectures, Data modeling, Design and modelling, Image segmentation

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Ultrasound imaging is an effective screening tool for early diagnosis of breast tumor to decrease the mortality rate. However, differentiation of tumor type based on ultrasound images remains challenging in the field of medical imaging due to the inherent noise and speckles. Thus, obtaining additional information for lesion localization could better support the decision-making by clinicians and improve diagnosis fidelity. Recently, multi-task learning (MTL) methods have been proposed for joint tumor classification and localization, where promising results were demonstrated. However, most MTL methods trained independent network branches for the two different tasks, which might cause conflicts in optimizing features due to their different purposes. In addition, these methods usually require fully-segmented datasets for model training, which poses a heavy burden in data annotation. To overcome these limitations, we propose a novel MTL framework for joint breast tumor classification and localization, motivated by the idea of attention mechanism and weakly-supervised learning strategy. Our method has three major advantages. First, an auxiliary lesion-aware network (LA-Net) with multiple attention modules for lesion localization was designed on top of a pre-defined classification network. In this way, the extracted features for classification were directly augmented by the region of interest (ROI) predicted by the LA-Net, alleviating the potential conflicts between the two tasks. Second, a sequential training strategy with a weakly-supervised learning scheme was employed to train the LA-Net and the classification network iteratively, which allows the model to be trained on the partially-segmented datasets and reduces the burden on data annotation. Third, the LA-Net and classification network design are modularized so that both architectures can be flexibly adjusted for various applications. Results from experiments performed on two breast ultrasound image datasets demonstrated the effectiveness of the proposed method.

Proceedings Article | 15 March 2023 Poster + Paper

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data

Xiaohui Zhang, Eric Landsness, Joseph Culver, Jin-Moo Lee, Mark Anastasio

Proceedings Volume 12365, 123650B (2023) https://doi.org/10.1117/12.2649375

KEYWORDS: Deep learning, Machine learning, Classification systems, Calcium, Data modeling, Brain, Network architectures, Feature extraction, Convolutional neural networks, Spatial learning, Reliability, Electroencephalography

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Proceedings Article | 28 April 2022 Poster + Presentation

Identifying functional brain networks from spatial-temporal wide-field calcium imaging data via a recurrent autoencoder

Xiaohui Zhang, Eric Landsness, Joseph Culver, Mark Anastasio

Proceedings Volume PC11946, PC1194612 (2022) https://doi.org/10.1117/12.2626317

KEYWORDS: Calcium, Brain, Neuroimaging, Temporal resolution, Signal to noise ratio, Neural networks, Network architectures, Independent component analysis, Hemodynamics

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SPIE Journal Paper | 16 November 2021 Open Access

Impact of deep learning-based image super-resolution on binary signal detection

Xiaohui Zhang, Varun Kelkar, Jason Granstedt, Hua Li, Mark Anastasio

JMI, Vol. 8, Issue 06, 065501, (November 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.6.065501

KEYWORDS: Signal detection, Lawrencium, Super resolution, Binary data, Information operations, Network architectures, Medical imaging, Surgery, Signal attenuation, Image quality

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Proceedings Article | 8 March 2021 Presentation + Paper

An optical, endoscopic brush for high-yield diagnostics in esophageal cancer

S. Anandasabapathy, X. Zhang, M. Anastasio, R. Richards-Kortum, E. Petrova

Proceedings Volume 11620, 116200B (2021) https://doi.org/10.1117/12.2583301

KEYWORDS: Endoscopy, Diagnostics, Cancer, Super resolution, Algorithm development, Optical fibers, Motion models, Laser imaging, Image segmentation, Image processing

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Task-based evaluation of deep image super-resolution in medical imaging

Varun Kelkar, Xiaohui Zhang, Jason Granstedt, Hua Li, Mark Anastasio

Proceedings Volume 11599, 115990X (2021) https://doi.org/10.1117/12.2582011

KEYWORDS: Medical imaging, Super resolution, Image analysis, Image quality, Signal to noise ratio, Quality measurement, Neural networks, Diagnostics

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SPIE Journal Paper | 12 October 2017

Acoustic beam anomalies in automated breast imaging

Rungroj Jintamethasawat, Xiaohui Zhang, Paul Carlson, Marilyn Roubidoux, Oliver Kripfgans

JMI, Vol. 4, Issue 04, 045001, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.045001

KEYWORDS: Skin, Breast, Acoustics, Tissues, Ultrasonography, Signal attenuation, Breast imaging, Wave propagation, Transducers, Optical simulations

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