paperlist.ai

AI conference papers

AI conference paper library

Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

IJCAI 2026

Patient-Visit-Spanned Hypergraph Learning for EHR-based Diagnosis Prediction

Ye Yuan, Haiyan Wang, Lun Hu, Xin Luo

Hypergraphs effectively model complex interactions in structured Electronic Health Records (EHR). Consequently, Hypergraph Neural Networks (HGNNs) are commonly applied to EHR-based diagnosis prediction. However, existing HGNNs struggle to capture patient-visit long-range dependencies when processing EHR-derived hypergraphs. To tackle this issue, we propose a Patient-Visit-Spanned HyperGraph Learning (PVHGL) framework specifically designed for diagnosis prediction. Concretely, PVHGL initially constructs a unified patient-visit hypergraph that integrates visit records from all patients, enabling the capture of shared healthcare patterns across the patient population. Subsequently, it incorporates a Transformer architecture enhanced by two structure-encoding matrices to facilitate one-step message propagation, which preserves both local and global hypergraph structural properties effectively. Additionally, the framework integrates a medical code co-occurrence matrix to explicitly guide the learning process by highlighting critical medical code interactions. Comprehensive experiments on three real-world datasets demonstrate that the proposed PVHGL significantly outperforms state-of-the-art baselines in diagnosis prediction.

IJCAI 2026

HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction

Dahai Yu, Lin Jiang, Rongchao Xu, Guang Wang

Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced machine learning methods being employed for better prediction performance, existing works usually formulate this task as a time-series forecasting problem without considering the intrinsic spatial dependencies of different types of healthcare facilities, and they also fail to provide reliable predictions under abnormal situations such as public emergencies. To advance existing research, we propose HealthMamba, an uncertainty-aware spatiotemporal framework for accurate and reliable healthcare facility visit prediction. HealthMamba comprises three key components: (i) a Unified Spatiotemporal Context Encoder that fuses heterogeneous static and dynamic information, (ii) a novel Graph State Space Model called GraphMamba for hierarchical spatiotemporal modeling, and (iii) a comprehensive uncertainty quantification module integrating three uncertainty quantification mechanisms for reliable prediction. We evaluate HealthMamba on four large-scale real-world datasets from California, New York, Texas, and Florida. Results show HealthMamba achieves around 6.0% improvement in prediction accuracy and 3.5% improvement in uncertainty quantification over state-of-the-art baselines.

IJCAI 2026

GroupMIL: Semantic Group Based Multiple Instance Learning for Whole Slide Image Analysing

Zhao Yao, Zhenmi Xie, Mengxin Tian, Guoqing Wu, Yaonan Wang, Min Liu

Whole Slide Image (WSI) analysis faces challenges due to gigapixel resolutions and slide-level weak supervision. Multiple Instance Learning (MIL) serves as a pivotal method for this task. However, existing MIL frameworks often fail to exploit the inherent redundancy of tissue patterns or the semantic coherence among similar patches within a WSI. We propose GroupMIL, a novel framework that introduces a differentiable grouping mechanism into the MIL framework. This approach enables the automatic emergence of semantic segments using only slide-level labels. We specifically introduce a multi-stage grouping block and a hierarchical aggregator, which progressively fuse features within and across groups to construct a robust slide-level representation. Extensive experiments on multiple public datasets across cancer subtyping and survival prediction tasks demonstrate that GroupMIL consistently surpasses state-of-the-art performance.

IJCAI 2026

DNA-PPG: A Foundation Model for Photoplethysmography via Dual Neighborhood Alignment

Yizhang Yang, Jinshi Cui, Wenlong Wu, Chunlong Tu, Yang Zhang, Junshi Lu, LI Wang, Guosong Gao

Existing physiological foundation models face two limitations: rigid hard-negative sampling indiscriminately repels morphologically similar samples, distorting the natural manifold; and coarse discretization strategies sever the intrinsic continuity of physiological states, inducing precision loss. To address these challenges, we propose DNA-PPG, a novel pre-training framework anchored in Dual Neighborhood Alignment. DNA-PPG integrates the Morphology-Aware Self-Supervised Branch using Time-Frequency Soft Weighting to capture universal signal dynamics shared among diverse subjects, with the Physiological Semantic Alignment Branch that projects physiological indicators into continuous semantic space to explicitly embed precise physiological priors into the representation space. We scale the pre-training to 10.7 million PPG segments from over 8,400 subjects to ensure robust generalization. Extensive evaluations on six downstream benchmarks demonstrate that DNA-PPG significantly outperforms state-of-the-art baselines, achieving an 18% reduction in regression error and an 11.5% improvement in classification performance. These results validate DNA-PPG as a robust, universal feature extractor for diverse photoplethysmography applications.

IJCAI 2026

THGAgents: Traceable Biomedical Hypothesis Generation via Dynamic Causal Reasoning

Mingjian Yang, Kun Dong, Kevin Lim, Juan Liu

While Large Language Models (LLMs) offer promise in scientific discovery, leveraging LLMs to drive biomedical research requires the scientific discovery process to be performed in combination with cutting-edge biomedical research and rigorous mechanistic causal chains. As such, both current Retrieval-augmented generation methods lacking causal reasoning capabilities, and the static traditional knowledge graphs failing to reflect evolving scientific knowledge, present obstacles to utilizing LLMs as scientific discovery tools. In response to these ongoing challenges, we present THGAgents. THGAgents utilize collaborative and dynamically updating agents to build a Traceable Causal Knowledge Graph, which serves as the foundation for the evidence-based knowledge structure. Crucially, we employ an LLM-driven heuristic search algorithm to traverse the complex network, balancing both novelty and rigor to deduce strict, evidence-based mechanistic causal chains. Additionally, THGAgents utilize a generator-critic loop to support hypothesis refinement. In experimental benchmarks across both cancer systems and neuroscience, THGAgents achieved up to a 0.80 hit rate in predicting validated scientific discoveries, providing an almost 9.5% increase in hypothesis quality scores versus current state-of-the-art systems, and decreasing the mechanistic hallucination rate to 1.12%. Our code is available at https://github.com/yangCode-res/THGAgents/.

IJCAI 2026

FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints

Lishan Yang, Wei Emma Zhang, Nam Kha Nguyen, Po Hu, Yanjun Shu, Weitong Chen, Sim Mong Yuan

Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, participating institutions often possess heterogeneous computational resources, resulting in imbalanced LoRA ranks, which pose a major challenge for effective collaboration. In addition, real-world applications in domains such as healthcare and transportation frequently suffer from missing modalities due to user mistakes or device failures, which significantly degrade global model performance in federated settings. To the best of our knowledge, no prior work has addressed these two challenges simultaneously in federated VLLMs. To tackle these issues, we propose FediLoRA, a lightweight federated LoRA aggregation framework that effectively mitigates the impact of missing modalities in heterogeneous environment. FediLoRA is explicitly motivated by the observation that simple averaging and structured editing can jointly benefit both global and personalized models. Our approach achieves strong performance across multiple general-domain and medical-domain benchmark datasets. Additional experiments on healthcare data further demonstrate that FediLoRA is well-suited for practical, real-world deployment scenarios. Our code is released at https://github.com/gotobcn8/FediLoRA.

IJCAI 2026

OrthKD: Extracting Generalized Clinical Knowledge from Heterogeneous Teachers for Lightweight Deployment

Yi Xu, Cheng Chen, Mufan Cao

Deploying diabetic retinopathy (DR) screening models in primary care requires edge-efficient systems that remain accurate, safe, and reliable under domain shift. Multi-teacher knowledge distillation (KD) is a natural compression strategy, but existing approaches largely assume that all teachers provide equally trustworthy supervision. In our setting, this assumption fails: a strong CNN teacher (EfficientNet-B3, 0.876 QWK) and a weaker Transformer teacher (Swin-Base, 0.830 QWK) are complementary, yet the Transformer's logits can still mislead the student. We therefore propose OrthKD, a selective-trust distillation framework that transfers full supervision from the strong CNN, uses feature-only distillation from the weak ViT, and enforces orthogonality between teacher-specific student projections to encourage complementary rather than redundant evidence. This design preserves local lesion precision, injects global structural context, and improves robustness to distribution shift. On 132,049 retinal images, a 5.4M-parameter MobileNetV3 student reaches 0.885 QWK on EyePACS and improves zero-shot Messidor-2 performance from 0.507 to 0.728 QWK, while also achieving strong referral AUC and calibration. These results show that selectively distilling heterogeneous teachers can enable practical DR screening on resource-constrained devices.

IJCAI 2026

DIAM: Adaptive Drug Repositioning with Decoupled Biological Mechanism and Instance-Aware Modulation

Kerui Xu, Keyuan Xu, Shuheng Yin, Hang Qiu

Drug repositioning has emerged as an attractive drug development strategy with deep learning-based computational methods showing great potential in predicting Drug-Disease Associations (DDAs). However, dominant computational paradigms typically rely on Random Negative Sampling (RNS) and static embedding fusion, leading to two fundamental limitations. First, RNS treats unobserved pairs uniformly, resulting in coarse decision boundaries that fail to distinguish true associations from ambiguous candidates. Second, static fusion applies a monolithic combination of heterogeneous features, failing to adapt to the sample-specific dominance of different biological mechanisms. To address these issues, we propose DIAM, which establishes a mechanism-adaptive paradigm by explicitly decoupling structural and molecular signals. Specifically, DIAM introduces a Dual-Stream Biological Mechanism Decoupling module to construct global structural propagation and local molecular interaction views explicitly. Leveraging these views, we design a biological plausibility score to guide the hard negative sampling, enforcing finer-grained decision boundaries. Furthermore, an Adaptive Residual Gating (ARG) is devised to perform instance-aware modulation, dynamically weighing the contribution of global and local views for each specific pair. Extensive experiments on three benchmark datasets demonstrate that DIAM outperforms seven state-of-the-art methods. A case study on Alzheimer's disease further validates the model's effectiveness in identifying potential candidate drugs for practical application.

IJCAI 2026

Dual-Channel Semantic-Enhanced Combinatorial Medication Recommendation via Knowledge Distillation

Jiawei Wen, Jiabao Guo, Zhaohai Bai, Kaishun Wu, Ma Lin, Haodi Zhang

As a vital task in healthcare, combinatorial medication recommendation aims to generate drug combinations tailored to patient health status. Precisely capturing the rich semantic information within clinical narratives is crucial for achieving this goal. However, existing approaches primarily rely on isolated identifiers (i.e. patient IDs, drug codes), failing to leverage the inherent semantic associations between patient conditions and medication descriptions. To fill this gap, we propose the Dual-Channel Semantics-Enhanced Network (DCSENet), a novel dual-channel framework that explicitly incorporates context-rich clinical narratives knowledge. DCSENet fine-tunes domain-adapted pre-trained language models (LMs) to capture semantic correlations between patient status and medication narratives. A transformer-based dual-channel decoder decodes the semantic information at the disease-level and the patient-level respectively. The disease-level channel focuses on the natural text semantic associations between diseases and drugs, while the patient-channel provides personalized features. To mitigate the prohibitive computational overhead of the LMs in clinical deployment, we introduce an attention-map-based knowledge distillation mechanism that efficiently transfers semantic knowledge from the LMs into an identifier-based (ID-based) target model. Extensive experiments on MIMIC-Ⅲ and MIMIC-Ⅳ datasets demonstrate that DCSENet outperforms existing state-of-the-art methods in recommendation accuracy while maintaining a low computational cost.

IJCAI 2026

SAM-GPT: Hilbert Curve Enhanced Mamba for Brain Lesion Segmentation and VLM-based Analysis

Jinfu Wang, Qiyuan Wang, Yunfei Liang, Kaipeng Wang, Jinhua Zhao

Recent breakthroughs in Vision-Language Models (VLMs) have shown strong potential in medical analysis, but they remain limited in the brain lesion image domain, especially when pathological regions occupy a small portion of the image. This is because VLMs tend to put excessive attention on background regions that are visually similar to target regions. Based on the findings, we propose SAM-GPT, a novel framework that leverages segmentation-derived spatial priors to support VLM-based lesion classification. It first uses an enhanced segmentation model to localize pathological regions for diagnostic tasks, and then converts lesion attributes (e.g., size, pixel range, lesion location) into linguistic guidance for a vision–language model. To improve small lesion recognition, we incorporate a new Hilbert scanning method into Mamba that improves both local spatial continuity and global spatial modeling. Experiments on benchmark datasets show that our model achieves an average Dice coefficient of 72.80% on the brain lesion segmentation task and an accuracy of 80.56% on the brain disease classification task, demonstrating the effectiveness of the proposed framework. The code is available at https://github.com/1JF-Wang/sam-gpt.

IJCAI 2026

Structured Modality-Aware Token Interaction for Multimodal Medical Imaging

Selene Tomassini, Hafiza Ayesha Hoor Chaudhry, Alessandro Galdelli, Paolo Giorgini

Multimodal medical imaging benefits from the global context modeling of transformers, yet most existing models fuse modalities implicitly by channel concatenation, leaving cross-modal interaction unstructured or relying on costly multi-stream cross-attention. We propose Modality-Aware Token Interaction (MATI), an architecturally lightweight and backbone-agnostic module that structures multimodal interaction within a single token stream by partitioning embedding channels into modality-aligned subspaces. MATI performs modality-preserving intra-subspace self-attention and gated global mixing for controlled inter-subspace exchange. We instantiate MATI in UNETR and introduce two architectures: ModaUNETR^S refines selected skip-token representations, and ModaUNETR^E injects MATI into the transformer encoder to progressively shape the token hierarchy. Experiments on the BraTS 2020 benchmark employ five-fold cross-validation and report segmentation and efficiency metrics on the official training and validation sets. Both models consistently improve over UNETR across all tumor subregions, with larger gains for tumor core and whole tumor, and ModaUNETR^E further improves upon ModaUNETR^S. An ablation study confirms monotonic gains from structured interaction. Compared with heavier transformers, the proposed models achieve a favorable accuracy-efficiency trade-off without modality-specific encoders or quadratic cross-attention, supporting structured multimodal interaction as a first-class architectural principle. Implementation of MATI and its instantiations is available at https://github.com/S3l11/MATI.

IJCAI 2026

Singly-Connected Multiple Minimal Networks for Efficient Temporal Reasoning About Multiple Clinical Guideline Instantiations

Andrea Terenziani

Temporal constraints are an intrinsic component of most clinical guidelines. Several approaches to computerized clinical guidelines (CIGs) offer temporal reasoning facilities to support the execution of a CIG for a specific patient, mostly based on bounds on differences and on the Simple Temporal Problem framework. However, in scheduling activities (e.g., within a hospital), it is necessary to consider multiple executions of CIGs for different patients, which can also be (partly) related to each other. In this work we extend current temporal reasoning techniques to apply to such a context. We propose a new temporal constraint model, prove its properties, and exploit them to provide efficient management of patients' temporal constraints, and to support efficient query answering. We also propose an experimental evaluation, demonstrating the step forward with respect to current approaches. Notably, our approach is general, and can apply to all temporal reasoning problems having the same topology of the "multiple CIG execution" problem.

IJCAI 2026

QA-MoE: Quality-Aware and Stable Multimodal Mixture-of-Experts for Robust Clinical Prediction in Noisy and Missing-Modal Settings

Linpeng Sun, Victor S. Sheng

Clinical prediction increasingly relies on multi-modal inputs, where reliability and efficiency are crucial for real-world deployment. However, mainstream fusion and MoE gating typically treat all available modalities as uniformly beneficial and allow noisy or weakly informative modalities to perturb routing, leading to instability, routing collapse, and miscalibrated confidence under missingness and shift. We propose QA-MoE, a Quality-Aware and stable multimodal Mixture-of-Experts that decouples reliability estimation from routing to enable robust, sparse, and interpretable fusion. QA-MoE adopts a modular architecture where each modality is initially encoded into a shared embedding space. To deal with structurally missing data, we employ a completion pathway that maintains a consistent interface. Unlike standard approaches, QA-MoE separates reliability estimation from the routing process. We propose an Evidential Quality Scorer to measure epistemic uncertainty, which then guides a Stability-Enhanced Subset Selector to filter out noisy modalities on the fly. Additionally, we include a Ternary Expert Aggregation mechanism acting as a specialized branch to stabilize predictions when data missingness is severe. Evaluations on clinical benchmarks (ADNI for Alzheimer’s staging and MIMIC-IV for Length-of-Stay) demonstrate that QA-MoE outperforms strong multimodal baselines, improving reliability while cutting down unnecessary computation. This indicates that QA-MoE offers a robust solution for multimodal decision support, especially in clinical settings prone to noise and missing data.

IJCAI 2026

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences

Pengfei Song, Fangjin Liu, Wenwen Zeng, Yonghuang Wu, Chengqian Zhao, Feiyu Yin, Xuan Xie, Jinhua Yu

Contemporary glioma diagnosis integrates molecular features with histopathology to guide clinical decision-making. However, in clinical settings, divergent imaging protocols result in incomplete MRI sequences, leading to two primary challenges: forcing existing frameworks to discard a large portion of clinical data during training and consequently limiting their clinical applicability. To address these limitations, we propose GMENet, a Generative Mixture of Experts Network for multi-center glioma diagnosis with incomplete imaging sequences. Firstly, we design a Cross-attention-based Gated Generation Module that synthesizes missing sequence features from available sequences via cross-attention and dynamic gating mechanisms, incorporating a cycle-consistency loss to preserve semantic integrity. Secondly, we introduce a Dynamically Weighted Experts Fusion Module that performs mixture-of-experts interaction and confidence-aware fusion over original and synthesized dual-sequence features for multi-task prediction. We evaluate GMENet on a multi-center cohort of 1,241 subjects from four in-house datasets and two public repositories. Experiments show that GMENet expands clinically usable training data by 97%, relative to complete-sequence-only data. Furthermore, it consistently outperforms state-of-the-art methods trained on complete data, demonstrating improved robustness under cross-center distribution shifts. Code is available at: https://github.com/spf-sd/GMENet.

IJCAI 2026

Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting

Sijie Ruan, Jinyu Li, Jia wei, Zenghao Xu, Jie Bao, Junshi Xu, Junyang Qiu, Shuliang Wang, Xiaoxiao Wang, Hanning Yuan

Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidemic signals, over-simplified spatial relations, and unstable parameter estimation. To address these challenges, we propose the Spatio-Temporal priOr-aware Epidemic Predictor (STOEP), a novel hybrid framework that integrates implicit spatio-temporal priors and explicit expert priors. STOEP consists of three key components: (1) Case-aware Adjacency Learning (CAL), which dynamically adjusts mobility-based regional dependencies using historical infection patterns; (2) Space-informed Parameter Estimating (SPE), which employs learnable spatial priors to amplify weak epidemic signals; and (3) Filter-based Mechanistic Forecasting (FMF), which uses an expert-guided adaptive thresholding strategy to regularize epidemic parameters. Extensive experiments on real-world COVID-19 and influenza datasets demonstrate that STOEP outperforms the best baseline by 11.1% in RMSE. The system has been deployed at a provincial CDC in China to facilitate downstream applications.

IJCAI 2026

SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking

Adam Remaki, Christel Gérardin, Eulàlia Farré-Maduell, Martin Krallinger, Xavier Tannier

We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in supervised biomedical entity linking (BEL): the scarcity of expert-annotated training data. SynCABEL leverages large language models to generate context-rich synthetic training examples for all candidate concepts in a target knowledge base, providing broad supervision without manual annotation. We demonstrate that SynCABEL, when combined with decoder-only models and guided inference establish new state-of-the-art results across three widely used multilingual benchmarks: MedMentions for English, QUAERO for French, and SPACCC for Spanish. Evaluating data efficiency, we show that SynCABEL reaches the performance of full human supervision using up to 60% less annotated data, substantially reducing reliance on labor-intensive and costly expert labeling. Finally, acknowledging that standard evaluation based on exact code matching often underestimates clinically valid predictions due to ontology redundancy, we introduce an LLM-as-a-judge protocol. This analysis reveals that SynCABEL significantly improves the rate of clinically valid predictions. Our synthetic datasets, models, and code are released to support reproducibility and future research. • HuggingFace Datasets & Models • GitHub Repository

IJCAI 2026

Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning

Xueqiao Peng, Andrew Perrault

Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited resources, particularly in the early outbreak stages. In real-world public health settings, resources must be allocated across multiple outbreak clusters that emerge asynchronously, vary in size and risk, and compete for a shared resource budget. We define a cluster as a group of close contacts generated by a single infected index case. Thus, decisions must be made under uncertainty and heterogeneous demands while respecting operational constraints. We formulate this problem as a constrained restless multi-armed bandit and propose a hierarchical reinforcement learning framework. A global controller learns a continuous action cost multiplier that adjusts global resource demand, while a generalized local policy estimates the marginal value of allocating resources to individuals within each cluster. We evaluate the proposed framework in a realistic agent-based simulator of SARS-CoV-2 with dynamically arriving clusters. Across a wide range of system scales and testing budgets, our method consistently outperforms RMAB-inspired and heuristic baselines, improving outbreak control effectiveness by 20--30%. Experiments on up to 40 concurrently active clusters further demonstrate that the hierarchical framework is highly scalable and enables faster decision-making than the RMAB-inspired method.

IJCAI 2026

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models

Minh Khoi Nguyen, Dai Lam Le, Amir Reza Jafari, Tuan Dung Nguyen, Hong Son Mai, Huy Thong Mai, Quang-Huy Nguyen, Thanh Trung Nguyen, Reza Farahbakhsh, Noel Crespi, Phi Le Nguyen

Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising significant safety concerns. Existing medical hallucination benchmarks primarily focus on 2D imaging with one-shot diagnostic questions, offering limited insight into whether predictions are grounded in correct localization and abnormality identification, allowing critical reasoning errors to remain hidden behind seemingly correct diagnoses. We introduce Med-StepBench, the first large-scale benchmark for step-wise hallucination detection in 3D oncological PET/CT, comprising over 12,000 images and more than 1,000,000 image–statement pairs across volumetric and multi-view 2D data, which decomposes clinical reasoning into four expert-designed diagnostic stages. Using clinician-verified annotations, we perform the first step-level evaluation of general-purpose and medical VLMs, revealing systematic failure modes obscured by aggregate accuracy metrics. Furthermore, we show that current VLMs are highly susceptible to adversarial yet clinically plausible intermediate explanations, which significantly amplify hallucinations despite contradictory visual evidence. Together, our findings highlight fundamental limitations in grounding multi-step clinical reasoning and establish Med-StepBench as a rigorous benchmark for developing safer and more reliable medical VLMs.

IJCAI 2026

Structure-Aware Contrastive Learning for Biomedical Embeddings: Bridging the Gap Between HPO and Clinical Literature

Jose Luis Mellina Andreu, Alejandro Cisterna García, Juan Botía

Large Language Models (LLMs) are extensively used at biomedical text processing but often fail to capture the complex, functional relationships encoded in expert knowledge graphs like the Human Phenotype Ontology (HPO). This "semantic gap'" limits their utility in precision medicine tasks such as rare disease diagnosis, where distinguishing overlapping clinical presentations requires understanding underlying pathophysiological connections rather than just surface-level textual similarity. In this work, we propose a Neuro-Symbolic Alignment Framework that bridges this separation by integrating literature-mined specialized phenotypical descriptions with the ontological structure used as reference. Specifically, we augment phenotype representations with automatically selected text fragments from massive corpus of descriptions mined from scientific literature (PubMed), overcoming the typical data scarcity of standard ontology definitions. We define a new embedding adaptation procedure whose fine-tuning approach is guided by a novel "Disease-Overlap" similarity measure, which prioritizes clinical co-occurrence of phenotypes over taxonomic distance, and optimizes the embedding space using AnglE Loss to mitigate gradient saturation. Extensive evaluations show that our approach significantly outperforms state-of-the-art baselines, including SapBERT, on both intrinsic semantic correlation and practical downstream tasks, including synthetic patient disease ranking and solving real cases stored in Phenopacket, where our model achieves x4 top-1 accuracy than the previous best model.

IJCAI 2026

Hierarchical Conditional Energy Modeling for Medical Vision–Language Pretraining

Chengsheng Mao, Yuan Luo

Contrastive vision–language pretraining models such as CLIP align images and text in a shared embedding space but do not explicitly model or evaluate the hierarchical semantics common in medical image interpretation. We propose HCE-CLIP (Hierarchical Conditional Energy CLIP), a vision–language pretraining framework that formulates medical image–text alignment as a hierarchical label-conditional energy modeling problem. HCE-CLIP encodes an image series using transformer-based aggregation and aligns it with free-text reports and structured label state prompts across multiple semantic levels. At each level, conditional energy functions favor clinically consistent label states while suppressing contradictory alternatives, enabling uncertainty-aware inference. To assess semantic coherence, we introduce a hierarchical contradiction-based metric that quantifies logical inconsistencies between fine-grained disease predictions and higher-level clinical summaries. Experiments on MIMIC-CXR and other public benchmarks show that HCE-CLIP outperforms existing medical vision–language pretraining methods in seen-label, zero-shot and linear-probe settings, while producing substantially fewer hierarchical contradictions.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.