The study presents a multimodal machine‑learning framework that combines genomic sequences, epidemiological indicators, wastewater surveillance, and clinical rash images to detect measles and infer transmission pathways. In a three‑stage pipeline, the authors achieved high accuracy in classifying transmission linkage (AUC 0.924, F1 0.912), moderate success in predicting outbreak risk (AUC 0.707), and near‑perfect recall for aligning genomic and imaging embeddings (Recall@1 0.985). The results highlight the complementary strengths of genomic data for linkage prediction and wastewater data for outbreak‑risk forecasting.
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).
arXiv:2610.07087v1 Announce Type: new Abstract: Despite rapid advances in artificial intelligence, reliable real-world plant disease detection remains a persistent challenge. Visual and deep learning...
arXiv:2610.35704v1 Announce Type: new Abstract: Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, parti...
Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, particularly for predicting biopsy outcomes remains li...
arXiv:2509.23552v2 Announce Type: replace-cross Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...
arXiv:2605. 25050v2 Announce Type: replace-cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets.
The paper introduces the FAPT-M Dataset, a pathology‑confirmed multimodal collection of 910 breast patients that includes ultrasound images, structured clinical attributes, and diagnostic descriptions. It proposes a clinically guided multimodal framework that fuses DenseNet visual encoding, CLIP‑inspired text encoding, and lightweight clinical encoding, enhanced by clinical‑conditioned adaptive modulation, cross‑modal Transformer fusion, and dual‑path representation learning. The method achieves 77.64% accuracy, 73.38% F1‑score, and 89.74% AUC, outperforming CNN, Transformer, and vision‑language baselines, and demonstrates the value of three‑modality fusion through ablation and class‑balanced studies.
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
The paper introduces MedCertAIn, a framework that uses data‑driven priors to enhance uncertainty estimation in multimodal clinical models. By integrating cross‑modal similarity and modality‑specific corruptions into neural network priors, the authors improve predictive performance and selective prediction for in‑hospital mortality risk using MIMIC‑IV and MIMIC‑CXR data. The results demonstrate competitive accuracy and notable gains over deterministic and stochastic baselines, underscoring the potential of such priors for reliable, uncertainty‑aware clinical decision support.
The paper introduces Prototype Purification and Regulation (PPR), a multi‑label few‑shot learning framework for medical image classification that addresses two key limitations of existing metric‑based meta‑learning methods. PPR first purifies prototypes by using sample‑level comorbidity scores to highlight disease‑specific features, then regulates inter‑class prototype distances with disease‑level comorbidity statistics to create a comorbidity‑aware embedding space. Experiments on four chest X‑ray datasets, including cross‑domain tests, show that PPR outperforms state‑of‑the‑art methods, improving disease detection and demonstrating robust generalization and clinical applicability.
arXiv:2609.26105v1 Announce Type: cross Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer pati...