Singapore

Semi-supervised learning (SSL) has become a promising direction for medical image segmentation, enabling models to learn from limited labeled data alongside abundant unlabeled samples. However, existing SSL approaches for multi-modal medical imaging often struggle to exploit the complementary information between modalities due to semantic discrepancies and misalignment across MRI sequences. To address this, we propose a novel semi-supervised multi-modal framework that explicitly enhances modality-specific representations and facilitates adaptive cross-modal information fusion. Specifically, we introduce a Modality-specific Enhancing Module (MEM) to strengthen semantic cues unique to each modality via channel-wise attention, and a learnable Complementary Information Fusion (CIF) module to adaptively exchange complementary knowledge between modalities. The overall framework is optimized using a hybrid objective combining supervised segmentation loss and cross-modal consistency regularization on unlabeled data. Extensive experiments on the BraTS 2019 (HGG subset) demonstrate that our method consistently outperforms strong semi-supervised and multi-modal baselines under 1%, 5%, and 10% labeled data settings, achieving significant improvements in both Dice and Sensitivity scores. Ablation studies further confirm the complementary effects of our proposed MEM and CIF in bridging cross-modality discrepancies and improving segmentation robustness under scarce supervision.

AAAI 2026

Modality-Specific Enhancement and Complementary Fusion for Semi-Supervised Multi-Modal Brain Tumor Segmentation

medical and biological imaging

segmentation

multimodal learning

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Recent advances in foundation models have enabled their integration into high-stakes clinical settings, particularly in computational pathology, where domain-specialized FMs demonstrate strong generalization. However, real-world deployment is constrained by their poorly calibrated uncertainty awareness and degraded performance in low-data regimes requiring few-shot adaptation strategies, leading to unreliable and inefficient diagnostic workflows. Conformal Prediction (CP) is an uncertainty quantification framework that offers distribution‑free, finite‑sample coverage guarantees for ensuring safer deployment in such settings. In this work, we explore the integration of various CP methods with pathology foundation models using three few-shot adaption strategies for classification tasks across two datasets. To assess the clinical effectiveness of these approaches, we propose four novel metrics aimed at improving clinical reliability and alleviating diagnostic workload in few-shot settings. Our results demonstrate that Conformal Prediction methods enhance the reliability of pathology foundation models and offer actionable uncertainty estimates to enable safe and efficient deployment in few-shot pathological classification workflows, with the LAC method achieving the best overall performance.

Towards Reliable Few-Shot Adaptation of Pathology Foundation Models via Conformal Prediction

Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. Medical Visual Question Answering (MedVQA) has particularly benefited from these developments. However, despite Bangla being one of the most widely spoken languages globally, there exists no established MedVQA benchmark for it. To address this gap, we introduce BanglaMedVQA, a dataset comprising clinically validated image–question–answer pairs, along with a comprehensive evaluation of current foundation models on this resource. Consistent with prior findings that report low performance of current models on English MedVQA benchmarks, our analysis reveals that Bangla performance is substantially lower, reflecting the challenges inherent to low-resource languages. Even top-performing models such as Gemini and GPT-4.1 mini fail to accurately answer specialized diagnostic questions, indicating severe limitations in fine-grained medical reasoning. Although certain open-source models, such as Gemma-3, occasionally outperform these models in general categories, they too struggle with clinically complex questions underscoring the urgent need for top-notch evaluation method.

How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking

We study an ultrasound-first, radiation-preserving policy for developmental dysplasia of the hip (DDH) that requests a radiograph only when needed. We (i) pretrain modality-specific encoders (ResNet‑18) with SimSiam on a large unlabelled registry (37{,}186 ultrasound; 19{,}546 radiographs), (ii) freeze the backbones and fit small, measurement-faithful heads on DDH relevant landmarks and measurements (iii) calibrate a one-sided conformal deferral rule on ultrasound predictions that guarantees coverage on a held-out calibration set. Ultrasound heads predict Graf and femoral head coverage; X-ray heads predict acetabular index (AI), center–edge (CE) angle, and IHDI grade . On our held out labeled evaluation set, ultrasound measurement error is modest (e.g., MAE 9.7, coverage MAE 14.0%), while radiographic probes achieve AI and CE MAEs of 7.6 and 8.9, respectively. The calibrated US-only policy is explored across rule families (alpha-only; alpha OR coverage; alpha AND coverage), uncertainty inflation factors, and per-utility trade-offs using decision-curve analysis. Conservative settings yield high coverage (e.g., 0.90 for ) with near-zero US-only rates; permissive settings (e.g., alpha OR coverage at larger deltas) achieve non-zero US-only throughput with expected coverage trade-offs. The result is a simple, reproducible pipeline that turns limited labels into interpretable measurements and tunable selective imaging curves suitable for clinical handoff and future external validation.

Radiation-Preserving Selective Imaging for Pediatric Hip Dysplasia: A Cross-Modal Ultrasound–Xray Policy with Limited Labels

Malaria remains a major health challenge in Africa, with accurate species identification critical for prompt treatment and control, especially in resource-limited settings like Rwanda. This study systematically benchmarks state-of-the-art detection architectures for automated multi-species malaria parasite detection from high-resolution Giemsa-stained blood smear images, addressing the persistent problems of class imbalance and the difficulty of detecting small Plasmodium falciparum ring forms. We evaluate and optimize YOLO-SPAM, YOLO-Para, and YOLOv12 models, applying a novel species-specific augmentation protocol with copy-paste and noise injection. YOLOv12, trained with these protocols, achieves outstanding overall performance ( , and demonstrates significant improvement in detecting small, clinically relevant P. falciparum parasites over non-attention YOLO methods). Comparative experiments reveal that targeted data augmentation and strategic model selection can overcome significant class imbalance, achieving reliable multi-species differentiation and precise localization even for morphologically subtle classes. Our findings validate the practical promise of advanced object detection models, such as YOLOv12, for malaria diagnosis workflows and support their future deployment in real-world microscopy labs in Rwanda and similar endemic regions.

Bridging the Gap in Malaria Diagnostics: An Attention-Centric YOLO Framework with Species-Specific Augmentation for Tiny Parasite Detection in Low-Resource Settings

Antimicrobial resistance (AMR) is becoming a bigger threat to global health, especially in low and middle-income countries (LMICs) where antibiotics are often given out without a prescription. This study presents a hybrid digital Artificial Intelligence(AI) framework aimed at improving rational antibiotic utilization by integrating pharmacies, telemedicine practitioners, and policymakers via an integrated digital infrastructure. The digital intervention system was implemented in 28 community pharmacies and showed significant improvements in how people used antibiotics. For example, the percentage of people who self-medicated with antibiotics dropped from 4.05% to 0.86%, and the percentage of people who bought antibiotics with a prescription jumped from 27.6% to 43.5%. We propose an Artificial Intelligence(AI) framework that combines a machine learning (ML)-based and a large language model (LLM)-based symptom checker for intelligent triage and clinical decision support, based on this foundation. These models will allow for efficient analysis of both structured and narrative symptom data, which will help pharmacists give advice and doctors refer patients right away. The suggested model demonstrates a scalable, data-driven, and human-in-the-loop approach to antibiotic stewardship, with the potential to significantly enhance AMR mitigation and healthcare accessibility in LMIC settings.

Integrating AI-Driven Triage into Digital Pharmacy Systems for Rational Antibiotic Use in Low-Resource Settings.

Explainability, domain generalization, and rare-class reliability are critical challenges in medical AI, where deep models often fail under real-world distribution shifts and exhibit bias against infrequent clinical conditions. This paper introduces XAI-MeD, an explainable medical AI framework that integrates clinically accurate expert knowledge into deep learning through a unified neuro-symbolic architecture. XAI-MeD is designed to improve robustness under distribution shift, enhance rare-class sensitivity, and deliver transparent, clinically aligned interpretations. The framework encodes clinical expertise as logical connectives over atomic medical propositions, transforming them into machine-checkable, class-specific rules. Their diagnostic utility is quantified through weighted feature satisfaction scores, enabling a symbolic reasoning branch that complements neural predictions. A confidence-weighted fusion integrates symbolic and deep outputs, while a Hunt-inspired adaptive routing mechanism— guided by Entropy Imbalance Gain (EIG) and Rare-Class Gini mitigates class imbalance, high intra-class variability, and uncertainty. We evaluate XAI-MeD across diverse modalities, on four challenging tasks: (i) Seizure Onset Zone (SOZ) localization from rs-fMRI, (ii) Diabetic Retinopathy grading, (iii) cardiac function assessment from echocardiograms, and (iv) ECG-based cardiac analysis. Experiments across ten multicenter datasets demonstrate substantial performance improvements, including 6% gains in cross-domain generalization and a rare-class F1 score of 0.916, far outperforming state-of-the-art deep learning baselines (F1 = 0.63). Ablation studies confirm that the clinically grounded symbolic components act as effective regularizers, ensuring robustness to distribution shifts. XAI-MeD thus provides a principled, clinically faithful, and interpretable approach to multimodal medical AI.

XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging

We present a training-free graph-based approach for solving interactive reasoning tasks in the ARC-AGI-3 benchmark. ARC-AGI-3 comprises game-like tasks where agents must infer task mechanics through limited interactions, and adapt to increasing complexity as levels progress. Success requires forming hypotheses, testing them, and tracking discovered mechanics. The benchmark has revealed that state-of-the-art LLMs are currently incapable of reliably solving these tasks. Our method combines vision-based frame processing with systematic state-space exploration using graph-structured representations. It segments visual frames into meaningful components, prioritizes actions based on visual salience, and maintains a directed graph of explored states and transitions. By tracking visited states and tested actions, the agent prioritizes actions that provide the shortest path to untested state-action pairs. On the ARC-AGI-3 Preview Challenge, this structured exploration strategy solves a median of 30 out of 52 levels across six games and ranks 3rd on the private leaderboard, substantially outperforming frontier LLM-based agents. Beyond its standalone performance, our approach can be viewed as a symbolic external tool: on ARC-AGI-3, it provides an explicit state graph that compensates for current LLMs failure to maintain and exploit such structure, suggesting a concrete way to augment LLM-based reasoners on interactive logical tasks. Taken together, these results indicate that explicit graph-structured exploration, even without learning, can serve as a strong baseline for interactive reasoning and underscore the importance of systematic state tracking and action prioritization in sparse-feedback environments.

Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks

Alzheimer's disease intervention planning requires both predictive modeling of biomarker trajectories and counterfactual reasoning about treatment timing. We propose a neuro-symbolic architecture that integrates Fourier Neural Operators (FNOs) for physics-informed biomarker prediction with Answer Set Programming (ASP) and SMT solving for verifiable intervention planning. Our approach adapts FNO methods to learn surrogate operators for AT(N) biomarker cascade dynamics, enabling fast multi-year trajectory forecasting while preserving cascade constraints. The symbolic layer formalizes clinical knowledge using first-order logic and temporal logic rules, allowing ASP/s(CASP) to generate candidate intervention strategies that are verified against safety properties using Z3 SMT solving. This combination provides both the predictive power of deep learning and the formal guarantees of symbolic reasoning, addressing critical translational challenges in precision medicine for Alzheimer's disease.

Neuro-Symbolic AI for Alzheimer's Disease: Physics-Informed Biomarker Prediction and Verifiable Intervention Planning

Rising provider turnover results in frequently needing to rematch patients with available providers. However, the rematching process is cumbersome for both patients and health systems, resulting in labor-intensive and ad hoc reassignments. We propose a novel patient-provider matching approach to address this issue by offering patients limited provider menus. The goal is to maximize match quality across the system while preserving patient choice. We frame this as a novel variant of assortment optimization, where patient-specific provider menus are offered upfront, and patients respond in a random sequence to make their selections. This hybrid offline-online setting is understudied in previous literature and captures system dynamics across various domains. We first demonstrate that a greedy baseline policy--which offers all providers to all patients--can maximize the match rate but lead to low-quality matches. Based on this, we construct a set of policies and demonstrate that the best policy depends on problem specifics, such as a patient's willingness to match and the ratio of patients to providers. On real-world data, our proposed policy improves average match quality by 13\% over a greedy solution by tailoring assortments based on patient characteristics. Our analysis reveals a tradeoff between menu size and system-wide match quality, highlighting the value of balancing patient choice with centralized planning.

Assortment Optimization for Patient-Provider Matching

NP-Hard scheduling problems are increasingly more prevalent in our daily lives. Their inherent difficulty and larger scale constitute a real challenge to both Artificial Intelligence and Operational Research communities that continuously seek efficient approaches to solve these theoretical problems and their real-life variants. Existing approaches are either exact or heuristic. Exact algorithms, which generally tackle small-sized academic type scheduling problems, have the property of identifying an optimal solution if one exists in the state-space. Algorithms belonging to the exact class are either time-and-space limited or time-limited. Time-and-space limited algorithms, such as Branch-and-Bound algorithms, have exponential time and space complexities. That is, such algorithms can exhaust the computational memory for a given size of the input problem instance, further confirming their unsuitability for solving very large problem instances. Time-limited algorithms, such as Depth-First Search, have polynomial space complexity and an exponential time complexity. While such algorithms are unlikely to exhaust computational memory, their runtime may be very long time, thus, restricting their suitability to address large-scale problem instances too.

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Next from AAAI 2026

Towards Reliable Few-Shot Adaptation of Pathology Foundation Models via Conformal Prediction

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