Search Results for author: Bradley J. Erickson

Found 7 papers, 3 papers with code

Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection

no code implementations16 Jan 2024 Cooper Gamble, Shahriar Faghani, Bradley J. Erickson

The uncertainty-aware DL model was tested on 8, 401 definite and challenging cases to assess its ability to identify challenging cases.

Conformal Prediction

MedYOLO: A Medical Image Object Detection Framework

1 code implementation12 Dec 2023 Joseph Sobek, Jose R. Medina Inojosa, Betsy J. Medina Inojosa, S. M. Rassoulinejad-Mousavi, Gian Marco Conte, Francisco Lopez-Jimenez, Bradley J. Erickson

We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging.

Computed Tomography (CT) Object +2

Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research

1 code implementation15 Nov 2023 Bardia Khosravi, Frank Li, Theo Dapamede, Pouria Rouzrokh, Cooper U. Gamble, Hari M. Trivedi, Cody C. Wyles, Andrew B. Sellergren, Saptarshi Purkayastha, Bradley J. Erickson, Judy W. Gichoya

This study examines the impact of synthetic data supplementation, using diffusion models, on the performance of deep learning (DL) classifiers for CXR analysis.

Denoising

Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report

1 code implementation21 Oct 2022 Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani, Mana Moassefi, Sanaz Vahdati, Bradley J. Erickson

Although the majority of inpainting techniques for medical imaging data use generative adversarial networks (GANs), the performance of these algorithms is frequently suboptimal due to their limited output variety, a problem that is already well-known for GANs.

Denoising

Interactive segmentation of medical images through fully convolutional neural networks

no code implementations19 Mar 2019 Tomas Sakinis, Fausto Milletari, Holger Roth, Panagiotis Korfiatis, Petro Kostandy, Kenneth Philbrick, Zeynettin Akkus, Ziyue Xu, Daguang Xu, Bradley J. Erickson

Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction.

Computed Tomography (CT) Image Segmentation +3

Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning

no code implementations21 Nov 2016 Zeynettin Akkus, Issa Ali, Jiri Sedlar, Timothy L. Kline, Jay P. Agrawal, Ian F. Parney, Caterina Giannini, Bradley J. Erickson

Significance: Predicting 1p/19q status noninvasively from MR images would allow selecting effective treatment strategies for LGG patients without the need for surgical biopsy.

Image Registration Self-Learning +2

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