22 citations · 30 across the 14 of their papers we have counts for
7 papers · 1 filter
Label Uncertainty for Ultrasound Segmentation
Malini Shivaram, Gautam Rajendrakumar Gare, Laura Hutchins +11
In medical imaging, inter-observer variability among radiologists often introduces label uncertainty, particularly in modalities where visual interpretation is subjective. Lung ult…
LEARNER: Contrastive Pretraining for Learning Fine-Grained Patient Progression from Coarse Inter-Patient Labels
Jana Armouti, Nikhil Madaan, Rohan Panda +8
Predicting whether a treatment leads to meaningful improvement is a central challenge in personalized medicine, particularly when disease progression manifests as subtle visual cha…
Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks
Gautam Rajendrakumar Gare, Tom Fox, Pete Lowery +9
Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this par…
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection
Gautam Rajendrakumar Gare, Andrew Schoenling, Vipin Philip +4
We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse…
The Role of Pleura and Adipose in Lung Ultrasound AI
Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung +8
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency lin…
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound
Gautam Rajendrakumar Gare, Hai V. Tran, Bennett P deBoisblanc +2
With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have b…
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data
Gautam Rajendrakumar Gare, Jiayuan Li, Rohan Joshi +5
We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF…