52 citations · 59 across the 8 of their papers we have counts for
6 papers · 1 filter
Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification
Yucheng Lu, Hubert Dariusz Zając, Veronika Cheplygina +1
Transfer learning is crucial for medical imaging, yet the selection of source datasets often relies on researchers' intuition rather than systematic principles, which can impact th…
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
Yucheng Lu, Shunxin Wang, Dovile Juodelyte +1
In this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from…
On dataset transferability in medical image classification
Dovile Juodelyte, Enzo Ferrante, Yucheng Lu +3
Current transferability estimation methods designed for natural image datasets are often suboptimal in medical image classification. These methods primarily focus on estimating the…
Exploring connections of spectral analysis and transfer learning in medical imaging
Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor +1
In this paper, we use spectral analysis to investigate transfer learning and study model sensitivity to frequency shortcuts in medical imaging. By analyzing the power spectrum dens…
Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez +3
Transfer learning has become an essential part of medical imaging classification algorithms, often leveraging ImageNet weights. The domain shift from natural to medical images has…
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars
Jun-Sang Yoo, Dong-Wook Kim, Yucheng Lu +1
Recent methods for single image super-resolution (SISR) have demonstrated outstanding performance in generating high-resolution (HR) images from low-resolution (LR) images. However…