Mr. Zhimin Wang Profile

Zhimin Wang

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Publications (5)

Proceedings Article | 2 April 2024 Poster + Paper

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

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

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

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

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

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 | 7 April 2023 Poster + Paper

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

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

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

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.

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