PLZero: placeholder based approach to generalized zero-shot learning for multi-label recognition in chest radiographs
Abstract By leveraging large-scale image-text paired data for pre-training, the model can efficiently learn the alignment between images and text, significantly advancing the development of zero-shot learning (ZSL) in the field of intelligent medical image analysis. However, the heterogeneity betwee...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01717-4 |
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| Summary: | Abstract By leveraging large-scale image-text paired data for pre-training, the model can efficiently learn the alignment between images and text, significantly advancing the development of zero-shot learning (ZSL) in the field of intelligent medical image analysis. However, the heterogeneity between cross-modalities, false negatives in image-text pairs, and domain shift phenomena pose challenges, making it difficult for existing methods to effectively learn the deep semantic relationships between images and text. To address these challenges, we propose a multi-label chest X-ray recognition generalized ZSL framework based on placeholder learning, termed PLZero. Specifically, we first introduce a jointed embedding space learning module (JESL) to encourage the model to better capture the diversity among different labels. Secondly, we propose a hallucinated class generation module (HCG), which generates hallucinated classes by feature diffusion and feature fusion based on the visual and semantic features of seen classes, using these hallucinated classes as placeholders for unseen classes. Finally, we propose a hallucinated class-based prototype learning module (HCPL), which leverages contrastive learning to control the distribution of hallucinated classes around seen classes without significant deviation from the original data, encouraging high dispersion of class prototypes for seen classes to create sufficient space for inserting unseen class samples. Extensive experiments demonstrate that our method exhibits sufficient generalization and achieves the best performance across three classic and challenging chest X-ray datasets: NIH Chest X-ray 14, CheXpert, and ChestX-Det10. Notably, our method outperforms others even when the number of unseen classes exceeds the experimental settings of other methods. The codes are available at: https://github.com/jinqiwen/PLZero . |
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| ISSN: | 2199-4536 2198-6053 |