activity
20162024
most citedData augmentation using learned transformations for one-shot medical image segmentation

57 citations · 93 across the 17 of their papers we have counts for

collaborators

28 papers

cs.LG2024★ 1 cited

Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction

Matt Y Cheung, Tucker J Netherton, Laurence E Court +2

Modern deep learning reconstruction algorithms generate impressively realistic scans from sparse inputs, but can often produce significant inaccuracies. This makes it difficult to…

cs.CV2023

GELDA: A generative language annotation framework to reveal visual biases in datasets

Krish Kabra, Kathleen M. Lewis, Guha Balakrishnan

Bias analysis is a crucial step in the process of creating fair datasets for training and evaluating computer vision models. The bottleneck in dataset analysis is annotation, which…

eess.IV2023★ 6 cited

ISLAND: Interpolating Land Surface Temperature using land cover

Yuhao Liu, Pranavesh Panakkal, Sylvia Dee +3

Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operation…

cs.CV2023★ 1 cited

Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation

Hao Liang, Pietro Perona, Guha Balakrishnan

We propose an experimental method for measuring bias in face recognition systems. Existing methods to measure bias depend on benchmark datasets that are collected in the wild and a…

eess.IV2023★ 1 cited

CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy

Yiran Sun, Tucker Netherton, Laurence Court +2

CT scans are the standard-of-care for many clinical ailments, and are needed for treatments like external beam radiotherapy. Unfortunately, CT scanners are rare in low and mid-reso…

cs.CV2023

F?D: On understanding the role of deep feature spaces on face generation evaluation

Krish Kabra, Guha Balakrishnan

Perceptual metrics, like the Fréchet Inception Distance (FID), are widely used to assess the similarity between synthetically generated and ground truth (real) images. The key idea…