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Junzheng Wu

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EAAI Journal 2026 Journal Article

Prior knowledge-guided adaptive multi-scale deep learning for Surface-Enhanced Raman Spectroscopy-based liver disease classification

  • Tianyi Lv
  • Xingen Gao
  • Juqiang Lin
  • Xianqiong Gong
  • Fuqiang Wang
  • Junzheng Wu
  • Hongyi Zhang
  • Nianyin Zeng

Surface-Enhanced Raman Spectroscopy (SERS) combined with deep learning demonstrates considerable potential for liver disease diagnosis. However, acquiring large-scale clinical datasets is challenging due to patient privacy constraints and sample collection complexity, leading to data scarcity that limits deep learning performance. Most existing methods rely heavily on data-driven approaches and fail to effectively utilize prior biomolecular knowledge, making them prone to overfitting. To address these limitations, we present a prior knowledge-guided adaptive multi-scale deep learning model that incorporates literature-validated biomolecular peak positions into feature learning. The model employs a dual-path architecture: an expert-guided path extracts structured features using adaptive multi-scale Gaussian convolutions optimized for distinct biomolecular markers, while a global context path captures comprehensive spectral information. An adaptive fusion mechanism integrates these paths to achieve synergy between prior knowledge and data-driven learning. In a five-class liver disease classification task with 215 subjects, our method achieved 93. 66% accuracy, a 5. 37% improvement over the baseline convolutional neural network (Baseline CNN, 88. 29%). Data constraint experiments demonstrated superior robustness; when training data was reduced to 20%, our approach maintained 86. 01% accuracy with a 10. 73 percentage point margin over the baseline. Furthermore, independent external validation on a cohort of 35 subjects yielded an overall accuracy of 82. 74%, significantly outperforming the Baseline CNN’s 68. 23% and reducing the generalization gap from 20. 15% to 10. 72%, validating the model’s robustness in cross-center clinical scenarios. This work provides an effective integration of domain knowledge with artificial intelligence for Surface-Enhanced Raman Spectroscopy-based medical diagnosis.

Details DOI

AAAI Conference 2026 Conference Paper

Reasoning via Implicit Self-supervised Emergence for Instruction Segmentation

  • Qing Zhou
  • Lichang Yang
  • Yuyu Jia
  • Junyu Gao
  • Weiping Ni
  • Junzheng Wu
  • Qi Wang

We challenge the assumption that complex instruction-guided segmentation tasks necessitate equally complex and explicit supervision. This paper introduces RISE (Reasoning via Implicit Self-supervised Emergence), a framework that learns intricate compositional reasoning, spanning spatial relations to world knowledge, without a single ground-truth mask. To achieve this, RISE employs reinforcement learning with GRPO guided by a single, strikingly simple reward: the semantic alignment score between the textual instruction and the predicted image region. Our primary discovery is the implicit emergence of a high-quality chain-of-thought process from this minimalist signal. Within a structured format, the model autonomously learns to understand instructions by accessing its latent knowledge, inferring spatial relationships—capabilities inherent in its architecture but unlocked by our simple objective. Remarkably, our emergent reasoning yields highly competitive results: RISE achieves 58.7 gIoU on the ReasonSeg benchmark, on par with methods using geometric rewards. Furthermore, we show extreme data efficiency: a variant trained on only 2,000 ImageNet-label pairs establishes a new state-of-the-art for annotation-free referring segmentation with 79.6 cIoU on RefCOCO.

PDF Details DOI
v2026.09.13