Dewa Ayu Defina Audrey Nathania

Low-Rank Adaptation Provides a Parameter-Efficient Trade-Off for Low-Data Medical Imaging

Adapting large pretrained Vision Transformers (ViTs) to medical imaging is challenging because labeled clinical data are limited and full fine-tuning is computationally expensive. This paper presents a multi-seed, multi-modality study of parameter-efficient fine-tuning (PEFT) for low-data binary medical image classific...

Deep Learning-Based Detection of Disease-Associated DNA motifs in Whole Exome Sequencing Data for Inherited Genetic Disorders

A Dual Explainability Framework for Cardiovascular Disease Prediction Using SHAP and LIME with Tree-Based Machine Learning Models