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pro vyhledávání: '"Milone, Diego"'
Deep learning methods are increasingly becoming instrumental as modeling tools in computational neuroscience, employing optimality principles to build bridges between neural responses and perception or behavior. Developing models that adequately repr
Externí odkaz:
http://arxiv.org/abs/2404.15390
Autor:
Gaggion, Nicolás, Matheson, Benjamin A., Xia, Yan, Bonazzola, Rodrigo, Ravikumar, Nishant, Taylor, Zeike A., Milone, Diego H., Frangi, Alejandro F., Ferrante, Enzo
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational an
Externí odkaz:
http://arxiv.org/abs/2311.13706
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity. In this context, auditing fairness o
Externí odkaz:
http://arxiv.org/abs/2309.00451
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
Autor:
Gaggion, Nicolás, Mosquera, Candelaria, Mansilla, Lucas, Saidman, Julia Mariel, Aineseder, Martina, Milone, Diego H., Ferrante, Enzo
The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of chest X-ray images have been released, most include disease diagnosis la
Externí odkaz:
http://arxiv.org/abs/2307.03293
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but
Externí odkaz:
http://arxiv.org/abs/2211.07395
Autor:
Chelotti, José O., Vanrell, Sebastián R., Martinez-Rau, Luciano S., Galli, Julio R., Utsumi, Santiago A., Planisich, Alejandra M., Almirón, Suyai A., Milone, Diego H., Giovanini, Leonardo L., Rufiner, H. Leonardo
Precision livestock farming optimizes livestock production through the use of sensor information and communication technologies to support decision making, proactively and near real-time. Among available technologies to monitor foraging behavior, the
Externí odkaz:
http://arxiv.org/abs/2204.00331
Publikováno v:
In Computers in Biology and Medicine December 2024 183
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or D
Externí odkaz:
http://arxiv.org/abs/2203.10977
This work aims to analyze standard evaluation practices adopted by the research community when assessing chest x-ray classifiers, particularly focusing on the impact of class imbalance in such appraisals. Our analysis considers a comprehensive defini
Externí odkaz:
http://arxiv.org/abs/2112.12843
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains. When the aim is to make predictions on distributions different from those seen at training, we inc
Externí odkaz:
http://arxiv.org/abs/2108.01621