Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Schroeder Joyce D"'
Autor:
Chaudhary, Muhammad F. A., Gerard, Sarah E., Christensen, Gary E., Cooper, Christopher B., Schroeder, Joyce D., Hoffman, Eric A., Reinhardt, Joseph M.
Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expirat
Externí odkaz:
http://arxiv.org/abs/2210.02625
Convolutional neural networks (CNNs) have been successfully applied to chest x-ray (CXR) images. Moreover, annotated bounding boxes have been shown to improve the interpretability of a CNN in terms of localizing abnormalities. However, only a few rel
Externí odkaz:
http://arxiv.org/abs/2207.09771
Autor:
Fenster Brett, Browning Jamey, Stalder Aurelien F, Glielmi Christopher, Silveira Lori, Buckner J, Kluiber Alex, Schroeder Joyce D, Hertzberg Jean
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 15, Iss Suppl 1, p P8 (2013)
Externí odkaz:
https://doaj.org/article/1c3e9583cb6f4174ab2bdf265f00f0c7
The interpretability of medical image analysis models is considered a key research field. We use a dataset of eye-tracking data from five radiologists to compare the outputs of interpretability methods and the heatmaps representing where radiologists
Externí odkaz:
http://arxiv.org/abs/2112.11716
Autor:
Chaudhary, Muhammad F. A., Gerard, Sarah E., Wang, Di, Christensen, Gary E., Cooper, Christopher B., Schroeder, Joyce D., Hoffman, Eric A., Reinhardt, Joseph M.
Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in ma
Externí odkaz:
http://arxiv.org/abs/2110.07878
Autor:
Lanfredi, Ricardo Bigolin, Zhang, Mingyuan, Auffermann, William F., Chan, Jessica, Duong, Phuong-Anh T., Srikumar, Vivek, Drew, Trafton, Schroeder, Joyce D., Tasdizen, Tolga
Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating
Externí odkaz:
http://arxiv.org/abs/2109.14187
Adversarial training, especially projected gradient descent (PGD), has proven to be a successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs have a prefer
Externí odkaz:
http://arxiv.org/abs/2009.04709
The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This information is critical for building trust in models and finding their biases.
Externí odkaz:
http://arxiv.org/abs/2007.01975
Autor:
Schroeder Joyce D, Hansel Nadia N, Barr R Graham, Silverman Edwin K, Hardin Megan, Make Barry J, Crapo James D, Hersh Craig P
Publikováno v:
Respiratory Research, Vol 12, Iss 1, p 127 (2011)
Abstract Background The coexistence of COPD and asthma is widely recognized but has not been well described. This study characterizes clinical features, spirometry, and chest CT scans of smoking subjects with both COPD and asthma. Methods We performe
Externí odkaz:
https://doaj.org/article/37f7473e71c44ea782ec86e02d74c228
Publikováno v:
International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019. p. 685-693
Knowledge of what spatial elements of medical images deep learning methods use as evidence is important for model interpretability, trustiness, and validation. There is a lack of such techniques for models in regression tasks. We propose a method, ca
Externí odkaz:
http://arxiv.org/abs/1908.10468