Dr. Hao Zhang Profile

Dr. Hao Zhang

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Proceedings Article | 25 March 2024 Paper

DAPFormer: dual-attention and pyramid-aware network for medical image segmentation

Yejin Yuan, Hao Zhang, Zhaoyu Xiong, Jiacheng Qin, Dan Xu

Proceedings Volume 13089, 130890H (2024) https://doi.org/10.1117/12.3021271

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Medical image segmentation aims to categorize pixels into different regions according to their corresponding tissues / organs in medical image. In recent years, due to Transformer's outstanding ability in the field of computer vision, various visual Transformers has been exploited in this task. However, these models often suffer from quadratic complexity in the self-attention and multi-scale information interaction. In this paper, we propose a novel dual attention and pyramid-aware network, DAPFormer, to solve the aforementioned limitations. It effectively combines efficient and channel attention into a dual attention mechanism to capture spatial and inter-channel relationships in the feature dimensions, meanwhile maintains computational efficiency. Additionally, we use pyramid-aware module to redesign the skip connection, modeling the cross-scale dependencies and addressing complex scale variations. Experiments on multi-organ cardiac and skin lesion segmentation datasets demonstrate that DAPFormer outperforms state-of-the-art methods.

Proceedings Article | 27 June 2023 Paper

Exploring affective image representation with visual attention and aesthetic fusion

Jixiong Ma, Hao Zhang, Kangjian He, Dan Xu

Proceedings Volume 12705, 127051U (2023) https://doi.org/10.1117/12.2680098

KEYWORDS: Emotion, Image analysis, Visualization, Convolution, Visual process modeling, Feature extraction, Image classification, Autoregressive models

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Affective image analysis aims to understand the sentiment of different images. The challenge is to develop a discriminative representation that bridges the affective gap between low-level features and high-level emotions. Most existing studies bridge the gap by designing deep models carefully to learn global representations in one shot directly or identify image emotion by extracting features at different levels in the model. They ignore that both local regions of an image and relationships between them impact emotional representation learning. This paper develops an affective image analysis method based on the aesthetic fusion hybrid attention network (AFHA). A modular hybrid attention block is designed to extract image emotion features and model long-range dependencies of images. By stacking hybrid attention blocks in ResNet-style, we obtain an affective representation backbone. Furthermore, considering that image emotion is inseparable from aesthetics, we employ a modified ResNet to extract image aesthetics. Finally, through a fusion strategy, the image's emotion is considered with the aesthetics conveyed. Experiments demonstrate the close relationship between emotion and aesthetics, and our plan has an excellent competitive effect compared with existing methods on the image sentiment analysis dataset.

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