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Weilun Wang

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JBHI Journal 2023 Journal Article

Grading of HCC Biopsy Images Using Nucleus and Texture Features

Goutam Chakraborty
Weilun Wang
Basabi Chakraborty
Shao-Kuo Tai
Yi-Shun Lo

Hepatocellular carcinoma (HCC) is one of the most critical health problems in the world. For proper treatment, it is important to identify the grade of cancer morbidity from HCC biopsy image. The diagnostic work is not only time-consuming but also subjective. The same biopsy image may be diagnosed as of different grades by different doctors, due to lack of experience or difference in opinion. In this work, we proposed an automatic grading system with classification accuracy matching to an experienced doctor, to help augment the diagnosis process. First, we proposed a segmentation method to isolate all nucleus-like objects present in a biopsy image. Non-target objects (here the target is a single HCC nucleus) present in the biopsy image are isolated too in the segmentation process. To eliminate such non-target objects, we proposed clustering of segmented images and a novel method to filter out target objects. Next, we proposed a two track neural network, where input consists of 2 different images. It combines a single segmented nucleus and a random cropped texture patch of the biopsy image to which the nucleus belongs. At this classifier output, we grade the single nucleus. Finally, a majority voting method is used to identify the grade of the whole biopsy image. We achieved an accuracy of 99. 03% for nucleus image grading and 99. 66% accuracy for grading biopsy images.

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NeurIPS Conference 2022 Conference Paper

Hand-Object Interaction Image Generation

Hezhen Hu
Weilun Wang
Wengang Zhou
Houqiang Li

In this work, we are dedicated to a new task, i. e. , hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status. This task is challenging and research-worthy in many potential application scenarios, such as AR/VR games and online shopping, etc. To address this problem, we propose a novel HOGAN framework, which utilizes the expressive model-aware hand-object representation and leverages its inherent topology to build the unified surface space. In this space, we explicitly consider the complex self- and mutual occlusion during interaction. During final image synthesis, we consider different characteristics of hand and object and generate the target image in a split-and-combine manner. For evaluation, we build a comprehensive protocol to access both the fidelity and structure preservation of the generated image. Extensive experiments on two large-scale datasets, i. e. , HO3Dv3 and DexYCB, demonstrate the effectiveness and superiority of our framework both quantitatively and qualitatively. The code will be available at https: //github. com/play-with-HOI-generation/HOIG.

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