533 citations
- Cornell UniversityUS77 papers
- Harvard UniversityUS11 papers
- Stanford UniversityUS9 papers
- CitigroupUS7 papers
- Columbia UniversityUS7 papers
- Massachusetts Institute of TechnologyUS7 papers
- Memorial Sloan Kettering Cancer CenterUS7 papers
- The University of Texas at AustinUS7 papers
- University of PennsylvaniaUS7 papers
- Georgia Institute of TechnologyUS6 papers
- Icahn School of Medicine at Mount SinaiUS6 papers
- Mayo ClinicUS6 papers
9 papers · 1 filter
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Gregory Holste, Yiliang Zhou, Song Wang +22
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more rela…
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
Taehyo Kim, Hai Shu, Qiran Jia +1
Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false discovery rate (FDR) control methods often ignore the spatial dependence among the voxe…
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
Gregory Holste, Ziyu Jiang, Ajay Jaiswal +12
Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. Howe…
A scoping review on multimodal deep learning in biomedical images and texts
Zhaoyi Sun, Mingquan Lin, Qingqing Zhu +4
Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the…
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
Suraj Rajendran, Weishen Pan, Mert R. Sabuncu +3
Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clin…