4 papers
Asking the Right Questions: Benchmarking Large Language Models in the Development of Clinical Consultation Templates
Liam G. McCoy, Fateme Nateghi Haredasht, Kanav Chopra +15
This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templ…
Deconver: A Deconvolutional Network for Medical Image Segmentation
Pooya Ashtari, Shahryar Noei, Fateme Nateghi Haredasht +4
While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in…
Quantization-aware Matrix Factorization for Low Bit Rate Image Compression
Pooya Ashtari, Pourya Behmandpoor, Fateme Nateghi Haredasht +3
Lossy image compression is essential for efficient transmission and storage. Traditional compression methods mainly rely on discrete cosine transform (DCT) or singular value decomp…
Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation
Ivan Lopez, Fateme Nateghi Haredasht, Kaitlin Caoili +2
Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data a…