361 citations
- University of California, Los AngelesUS8 papers
- Stanford UniversityUS6 papers
- Icahn School of Medicine at Mount SinaiUS4 papers
- University of California, San FranciscoUS4 papers
- University of EdinburghGB4 papers
- Cedars-Sinai Smidt Heart InstituteUS3 papers
- Child Health and Development InstituteUS3 papers
- Shahid Beheshti University of Medical SciencesIR3 papers
- The Queen's Medical Research InstituteGB3 papers
- University of WashingtonUS3 papers
- Brigham Young UniversityUS2 papers
- Columbia UniversityUS2 papers
31 papers
Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images
Mohammad Amin Khalafi, Seyed Amir Ahmad Safavi-Naini, Ameneh Salehi +13
Introduction: This study provides a comprehensive performance assessment of vision-language models (VLMs) against established convolutional neural networks (CNNs) and classic machi…
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models
Seyed Amir Ahmad Safavi-Naini, Shuhaib Ali, Omer Shahab +15
Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used…
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Arian Salahi-Niri +39
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimension…
Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry
Yuming Jiang, Devasahayam Arokia Balaya Rex, Dina Schuster +16
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up…
Improved inference for MCP-Mod approach for time-to-event endpoints with small sample sizes
Márcio A. Diniz, Diego I. Gallardo, Tiago M. Magalhães
The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework has been recently approved by the U.S. Food and Administration and European Medicines Agency as fit-…
Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems
Mateusz Godzik, Jacek Dajda, Marek Kisiel-Dorohinicki +5
Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relev…