9 citations · 16 across the 5 of their papers we have counts for
6 papers
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
Anwar Said, Roza G. Bayrak, Tyler Derr +4
Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psych…
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning
Tianyuan Yao, Chang Qu, Jun Long +13
With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more gene…
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning
Simon J. Ward, Tengfei Cao, Xiang Zhou +2
Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functiona…
Compound Figure Separation of Biomedical Images with Side Loss
Tianyuan Yao, Chang Qu, Quan Liu +11
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large…
CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns
Ruining Deng, Quan Liu, Shunxing Bao +5
Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biome…
PRAGMA: Interactively Constructing Functional Brain Parcellations
Roza G. Bayrak, Nhung Hoang, Colin B. Hansen +2
A prominent goal of neuroimaging studies is mapping the human brain, in order to identify and delineate functionally-meaningful regions and elucidate their roles in cognitive behav…