5k citations · 5k across the 7 of their papers we have counts for
7 papers
Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks
Ammar Ahmed Pallikonda Latheef, Alberto Santamaria-Pang, Craig K Jones +1
Brain networks display a hierarchical organization, a complexity that poses a challenge for existing deep learning models, often structured as flat classifiers, leading to difficul…
Evidential Uncertainty Quantification: A Variance-Based Perspective
Ruxiao Duan, Brian Caffo, Harrison X. Bai +2
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advan…
Applications of Sequential Learning for Medical Image Classification
Sohaib Naim, Brian Caffo, Haris I Sair +1
Purpose: The aim of this work is to develop a neural network training framework for continual training of small amounts of medical imaging data and create heuristics to assess trai…
Automated Artifact Detection in Ultra-widefield Fundus Photography of Patients with Sickle Cell Disease
Anqi Feng, Dimitri Johnson, Grace R. Reilly +5
Importance: Ultra-widefield fundus photography (UWF-FP) has shown utility in sickle cell retinopathy screening; however, image artifact may diminish quality and gradeability of ima…
Deep Labeling of fMRI Brain Networks
Ammar Ahmed Pallikonda Latheef, Sejal Ghate, Zhipeng Hui +4
Resting State Networks (RSNs) of the brain extracted from Resting State functional Magnetic Resonance Imaging (RS-fMRI) are used in the pre-surgical planning to guide the neurosurg…
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
Daniel D Kim, Rajat S Chandra, Jian Peng +14
Deep learning models have demonstrated great potential in medical 3D imaging, but their development is limited by the expensive, large volume of annotated data required. Active lea…