Search Results for author: Arkadiusz Sitek

Found 7 papers, 0 papers with code

Artificial Intelligence in PET: an Industry Perspective

no code implementations14 Jul 2021 Arkadiusz Sitek, Sangtae Ahn, Evren Asma, Adam Chandler, Alvin Ihsani, Sven Prevrhal, Arman Rahmim, Babak Saboury, Kris Thielemans

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications.

Image Reconstruction

FetalNet: Multi-task deep learning framework for fetal ultrasound biometric measurements

no code implementations14 Jul 2021 Szymon Płotka, Tomasz Włodarczyk, Adam Klasa, Michał Lipa, Arkadiusz Sitek, Tomasz Trzciński

The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur.

Pneumothorax and chest tube classification on chest x-rays for detection of missed pneumothorax

no code implementations14 Nov 2020 Benedikt Graf, Arkadiusz Sitek, Amin Katouzian, Yen-Fu Lu, Arun Krishnan, Justin Rafael, Kirstin Small, Yiting Xie

Chest x-ray imaging is widely used for the diagnosis of pneumothorax and there has been significant interest in developing automated methods to assist in image interpretation.

Classification General Classification +1

Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment

no code implementations28 Nov 2019 Skylar W. Wurster, Arkadiusz Sitek, Jian Chen, Karla Evans, Gaeun Kim, Jeremy M. Wolfe

Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images.

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

no code implementations19 Jul 2017 Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li

However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.

Computed Tomography (CT)

Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net

no code implementations29 May 2017 Songting Shi, Xiang Li, Arkadiusz Sitek, Quanzheng Li

In this article, we derive a Bayesian model to learning the sparse and low rank PARAFAC decomposition for the observed tensor with missing values via the elastic net, with property to find the true rank and sparse factor matrix which is robust to the noise.

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