7.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Mayo Clinic in FloridaUS31 papers
- Mayo ClinicUS27 papers
- Arizona State UniversityUS16 papers
- WinnMedUS10 papers
- University of Illinois Urbana-ChampaignUS9 papers
- Emory UniversityUS8 papers
- University of PennsylvaniaUS8 papers
- Harvard UniversityUS7 papers
- Cornell UniversityUS6 papers
- National Institutes of HealthUS6 papers
- Yale UniversityUS6 papers
- Indiana University – Purdue University IndianapolisUS5 papers
11 papers · 1 filter
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha +3
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of t…
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
Ricardo Gonzalez, Ashirbani Saha, Clinton J. V. Campbell +3
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Syst…
A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates
Jingru Fu, Daniel Ferreira, Örjan Smedby +1
Alzheimer's Disease and normal aging are both characterized by brain atrophy. The question of whether AD-related brain atrophy represents accelerated aging or a neurodegeneration p…
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede +8
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, partic…
Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance
DongAo Ma, Jiaxuan Pang, Michael B. Gotway +1
Deep learning nowadays offers expert-level and sometimes even super-expert-level performance, but achieving such performance demands massive annotated data for training (e.g., Goog…
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning
Alina Jade Barnett, Zhicheng Guo, Jin Jing +12
In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored…
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imagi…
Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition
Gang Wang, Qunxi Dong, Jianfeng Wu +11
Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the v…
Models Genesis
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang +2
Transfer learning from natural images to medical images has been established as one of the most practical paradigms in deep learning for medical image analysis. To fit this paradig…
SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis
Fei Gao, Teresa Wu, Jing Li +4
Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for bre…
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu +4
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure pro…