Mr. Zong Fan Profile

Zong Fan

at University of Illinois Urbana-Champaign

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Author

Publications (7)

Proceedings Article | 2 April 2024 Poster + Paper

Self-supervised learning based on StyleGAN for medical image classification on small labeled dataset

Xiaowei Wang, Hua Li, Zong Fan, Zhimin Wang, Chaojie Zhang, Muzaffer Özbey, Umberto Villa, Yao Hao, Zhongwei Zhang

Proceedings Volume 12926, 1292630 (2024) https://doi.org/10.1117/12.3006959

KEYWORDS: Education and training, Data modeling, Image classification, Medical imaging, Feature extraction, Performance modeling, Solid state lighting, Classification systems, Network architectures, Design

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Proceedings Article | 2 April 2024 Poster + Paper

Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images

Hua Li, Chaojie Zhang, Zong Fan, Zhimin Wang, Lulu Sun, Wade Thorstad, Hiram Gay, Xiaowei Wang, Yao Hao, Zhongwei Zhang, Mark Anastasio

Proceedings Volume 12926, 1292633 (2024) https://doi.org/10.1117/12.3007045

KEYWORDS: Education and training, Cancer, Transformers, Performance modeling, Tumor growth modeling, Histopathology, Image classification, Tumors, Data modeling, Tissues

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Proceedings Article | 2 April 2024 Presentation + Paper

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

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

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 | 7 April 2023 Poster + Paper

Deep-supervised adversarial learning-based classification for digital histologic images

Zhimin Wang, Zong Fan, Hua Li, Lulu Sun, Xiaowei Wang, Yao Hao, Hiram Gay, Wade Thorstad

Proceedings Volume 12471, 124711V (2023) https://doi.org/10.1117/12.2654402

KEYWORDS: Tumors, Image classification, Education and training, Image processing, Cancer, Data modeling, Feature extraction, Performance modeling, Pathology, Image restoration

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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

Shanshan Tang, Christine Lee, Zong Fan, Ping Gong, 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 | 4 April 2022 Presentation + Paper

Application of DatasetGAN in medical imaging: preliminary studies

Zong Fan, Hua Li, Varun Kelkar, Mark Anastasio

Proceedings Volume 12032, 120321O (2022) https://doi.org/10.1117/12.2611191

KEYWORDS: Image segmentation, Medical imaging, Data modeling, Feature extraction, Visualization, Performance modeling, Network architectures

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Proceedings Article | 15 February 2021 Presentation + Paper

Deep learning-based multi-class COVID-19 classification with x-ray images

Shenghua He, Zong Fan, Hua Li, Su Ruan, Xiaowei Wang

Proceedings Volume 11598, 1159813 (2021) https://doi.org/10.1117/12.2582261

KEYWORDS: Image classification, X-rays, X-ray imaging, Chest imaging, Convolutional neural networks, Radiography, Network architectures

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The COVID-19 pandemic continues spreading rapidly around the world and has caused devastating outcomes towards the health of the global population. The reverse transcription-polymerase chain reaction (RT-PCR) test, as the only current gold standard for screening infected cases, yields a relatively high false positive rate and low sensitivity on asymptomatic subjects. The use of chest X-ray radiography (CXR) images coupled with deep- learning (DL) methods for image classification, represents an attractive adjunct to or replacement for RT-PCR testing. However, its usage has been widely debated over the past few months and its potential effectiveness remains unclear. A number of DL-based methods have been proposed to classify the COVID-19 cases from the normal ones, achieving satisfying high performance. However, these methods show limited performance on the multi-class classification task for COVID-19, pneumonia and normal cases, mainly due to two factors: 1) the textures in COVID-19 CXR images are extremely similar to that of pneumonia cases, and 2) there are much fewer COVID-19 cases compared to the other two classes in the public domain. To address these challenges, a novel framework is proposed to learn a deep convolutional neural network (DCNN) model for accurately classifying COVID-19 and pneumonia cases from other normal cases by the use of CXR images. In addition to training the model by use of conventional classification loss which measures classification accuracy, the proposed method innovatively employs a reconstruction loss measuring image fidelity and an adversarial loss measuring class distribution fidelity to assist in the training of the main DCNN model to extract more informative features to support multi-class classification. The experiment results on a COVID-19 dataset demonstrate the superior classification performance of the proposed method in terms of accuracy compared to other existing DL-based methods. The experiment on another cancer dataset further implies the potential of applying the proposed methods in other medical imaging applications.

Showing 5 of 7 publications

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