no code implementations • 1 Dec 2022 • Hongzhi Zhu, Robert Rohling, Septimiu Salcudean
Deep learning, especially convolutional neural networks, has triggered accelerated advancements in computer vision, bringing changes into our daily practice.
1 code implementation • 14 Jun 2022 • Rohit Singla, Cailin Ringstrom, Grace Hu, Victoria Lessoway, Janice Reid, Christopher Nguan, Robert Rohling
This data set is a high-quality data set, including two sets of expert annotations, with a larger breadth of images than previously available.
no code implementations • 14 Jun 2022 • Rohit Singla, Ricky Hu, Cailin Ringstrom, Victoria Lessoway, Janice Reid, Christopher Nguan, Robert Rohling
Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease, or donor type.
1 code implementation • 15 Feb 2022 • Hongzhi Zhu, Robert Rohling, Septimiu Salcudean
To support the use of visual attention, this paper describes a novel deep learning model for visual saliency prediction on chest X-ray (CXR) images.
no code implementations • 15 Feb 2022 • Hongzhi Zhu, Septimiu Salcudean, Robert Rohling
However, no previous research has combined the network attention and human attention.
no code implementations • 23 Nov 2021 • Shahed Mohammed, Mohammad Honarvar, Qi Zeng, Hoda Hashemi, Robert Rohling, Piotr Kozlowski, Septimiu Salcudean
We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios.
1 code implementation • 3 Feb 2021 • Fatemeh Taheri Dezaki, Christina Luong, Tom Ginsberg, Robert Rohling, Ken Gin, Purang Abolmaesumi, Teresa Tsang
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements.
no code implementations • 2 Feb 2021 • Mohammad H. Jafari, Christina Luong, Michael Tsang, Ang Nan Gu, Nathan Van Woudenberg, Robert Rohling, Teresa Tsang, Purang Abolmaesumi
We tackle a specifically challenging problem, where training labels are noisy and highly sparse.
no code implementations • 2 Nov 2019 • Zhibin Liao, Hany Girgis, Amir Abdi, Hooman Vaseli, Jorden Hetherington, Robert Rohling, Ken Gin, Teresa Tsang, Purang Abolmaesumi
Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy.
1 code implementation • 16 Oct 2018 • Mehran Pesteie, Purang Abolmaesumi, Robert Rohling
We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM).