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pro vyhledávání: '"Lanfredi, Ricardo Bigolin"'
In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, som
Externí odkaz:
http://arxiv.org/abs/2403.04024
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
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:
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
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
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Akademický článek
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Autor:
Schroeder, Joyce D, Lanfredi, Ricardo Bigolin, Li, Tao, Chan, Jessica, Vachet, Clement, III, Robert Paine, Srikumar, Vivek, Tasdizen, Tolga
Publikováno v:
International Journal of COPD; Jan2021, Vol. 16, p3455-3466, 12p