22 citations · 28 across the 5 of their papers we have counts for
8 papers
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…
Activation Reward Models for Few-Shot Model Alignment
Tianning Chai, Chancharik Mitra, Brandon Huang +8
Aligning Large Language Models (LLMs) and Large Multimodal Models (LMMs) to human preferences is a central challenge in improving the quality of the models' generative outputs for…
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…
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…