Clinical Trials

MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction

Clinical trials are the gold standard for assessing the effectiveness and safety of drugs for treating diseases. Given the vast design space of drug molecules, elevated financial cost, and multi-year timeline of these trials, research on clinical trial outcome prediction has gained immense traction. Accurate predictions must leverage data of diverse modes such as drug molecules, target diseases,...

Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences

The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured text that is not easily understood by computer systems. Classifying and catego...

Sequential Classification of Aviation Safety Occurrences with Natural Language Processing

Safety is a critical aspect of the air transport system given even slight operational anomalies can result in serious consequences. To reduce the ch...

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation

Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts,...

MedCT: A Clinical Terminology Graph for Generative AI Applications in Healthcare

We introduce the world's first clinical terminology for the Chinese healthcare community, namely MedCT, accompanied by a clinical foundation model M...

Scale-up Unlearnable Examples Learning with High-Performance Computing

Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the hea...

Performance of YOLOv7 in Kitchen Safety While Handling Knife

Safe knife practices in the kitchen significantly reduce the risk of cuts, injuries, and serious accidents during food preparation. Using YOLOv7, an...

CROPS: Model-Agnostic Training-Free Framework for Safe Image Synthesis with Latent Diffusion Models

With advances in diffusion models, image generation has shown significant performance improvements. This raises concerns about the potential abuse o...

Open Problems in Machine Unlearning for AI Safety

As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and he...

Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency

Multimodal Large Language Models (MLLMs) have achieved impressive performance and have been put into practical use in commercial applications, but t...

Understanding, Implementing, and Supporting Security Assurance Cases in Safety-Critical Domains

The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, ...

[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].

To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant th...

Causal Machine Learning Methods for Estimating Personalised Treatment Effects -- Insights on validity from two large trials

Causal machine learning (ML) methods hold great promise for advancing precision medicine by estimating personalized treatment effects. However, thei...

SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets

Safety-critical driving data is crucial for developing safe and trustworthy self-driving algorithms. Due to the scarcity of safety-critical data in ...

SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation

As advancements in large language models (LLMs) continue and the demand for personalized models increases, parameter-efficient fine-tuning (PEFT) me...

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual ...

Systematic feature and architecture evaluation reveals tokenized learned embeddings enhance siRNA efficacy prediction

Recent advances in machine learning have improved the prediction of siRNA efficacy, with graph neural networks and transformer-based encodings leading...

CIAdex: Single-Cell FTIR Spectral Fingerprinting for Cell Identity Verification and Aging Quantification in Therapeutic Cell Manufacturing

Ensuring the identity and optimal aging state of cell products is critical for the efficacy and safety of cell therapies. Despite rapid iterations, th...

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment ...

Machine Learning Reveals Intrinsic Determinants of siRNA Efficacy

Small interfering RNAs (siRNAs) are widely used in therapeutics and agriculture for sequence-specific gene silencing. However, siRNA efficacy remains ...