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Shihao Su

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EAAI Journal 2026 Journal Article

Structural complementarity-aware molecular representation learning for medication recommendation

  • Shunpan Liang
  • Shuoqi Li
  • Shihao Su
  • Jun Li
  • Yanghao Xiao

To achieve accurate medication recommendations, recent studies have focused on extracting appropriate molecular structure embeddings to capture pharmacological mechanisms. Building upon two-dimensional (2D) modality representations, existing works incorporate three-dimensional (3D) structural information and employ contrastive learning to derive modality-invariant embeddings. In this paper, we identify two key limitations of existing multimodal molecular representation methods. First, different modalities of the same molecule contain shared features conveying common semantics and modality-specific features providing complementary structural information. Undifferentiated alignment strategies lead to the loss of modality-specific information. Second, naive concatenation of embeddings may cause modality collapse, where the richer 3D modality dominates and suppresses the 2D modality. To overcome these issues, we propose Structural Complementarity-Aware molecular representation learning for Medication recommendation (SCAMed), a framework designed to achieve precise alignment of shared information, effective extraction of structurally complementary modality-specific information, and balanced multimodal fusion. Specifically, during the representation extraction stage, we introduce an orthogonal decomposition module that separates the 2D and 3D molecular encodings into three disentangled components: 2D-specific, 3D-specific, and shared features. In the fusion stage, we design a reconstruction-based learning strategy that enforces the fused representation to accurately reconstruct the original 2D and 3D embeddings, effectively mitigating modality collapse. Finally, the fused molecular representations are integrated with patient Electronic Health Record (EHR) data for medication prediction. Comprehensive experiments on the Medical Information Mart for Intensive Care III (MIMIC-III) and Medical Information Mart for Intensive Care IV (MIMIC-IV) datasets demonstrate that our framework achieves substantial improvements over state-of-the-art baselines.

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AAAI Conference 2023 Conference Paper

PUPS: Point Cloud Unified Panoptic Segmentation

  • Shihao Su
  • Jianyun Xu
  • Huanyu Wang
  • Zhenwei Miao
  • Xin Zhan
  • Dayang Hao
  • Xi Li

Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points. Previous approaches treat semantic and instance segmentation as surrogate tasks, and they either use clustering methods or bounding boxes to gather instance groupings with costly computation and hand-craft designs in the instance segmentation task. In this paper, we propose a simple but effective point cloud unified panoptic segmentation (PUPS) framework, which use a set of point-level classifiers to directly predict semantic and instance groupings in an end-to-end manner. To realize PUPS, we introduce bipartite matching to our training pipeline so that our classifiers are able to exclusively predict groupings of instances, getting rid of hand-crafted designs, e.g. anchors and Non-Maximum Suppression (NMS). In order to achieve better grouping results, we utilize a transformer decoder to iteratively refine the point classifiers and develop a context-aware CutMix augmentation to overcome the class imbalance problem. As a result, PUPS achieves 1st place on the leader board of SemanticKITTI panoptic segmentation task and state-of-the-art results on nuScenes.

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