Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Ballester, Miguel A González"'
As current computing capabilities increase, modern machine learning and computer vision system tend to increase in complexity, mostly by means of larger models and advanced optimization strategies. Although often neglected, in many problems there is
Externí odkaz:
http://arxiv.org/abs/2406.03903
Autor:
Rasouligandomani, Morteza, del Arco, Alex, Villa, Tomaso, La Barbera, Luigi, Pellise, Ferran, Ballester, Miguel Angel Gonzalez, Galbusera, Fabio, Noailly, Jerome
Background: Proximal Junctional Failure (PJF) is a post-operative complication in adult spine surgery, often requiring reoperation. Osteotomy is often used in revision surgeries, leading to 34.8% complications. Hence, suboptimal decisions might be ex
Externí odkaz:
http://arxiv.org/abs/2402.13060
Autor:
Rasouligandomani, Morteza, del Arco, Alex, Chemorion, Francis Kiptengwer, Bisotti, Marc-Antonio, Galbusera, Fabio, Noailly, Jerome, Ballester, Miguel Angel Gonzalez
Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD throu
Externí odkaz:
http://arxiv.org/abs/2402.13041
Autor:
Comte, Valentin, Alenya, Mireia, Urru, Andrea, Recober, Judith, Nakaki, Ayako, Crovetto, Francesca, Camara, Oscar, Gratacós, Eduard, Eixarch, Elisenda, Crispi, Fàtima, Piella, Gemma, Ceresa, Mario, Ballester, Miguel A. González
Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditional deep learning-based automatic segmentation, although effective, requ
Externí odkaz:
http://arxiv.org/abs/2307.03579
Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice. A central aspect of uncertainty quantification is the ability of a model to return predictions that are well-ali
Externí odkaz:
http://arxiv.org/abs/2303.01099
Autor:
de Vente, Coen, Vermeer, Koenraad A., Jaccard, Nicolas, Wang, He, Sun, Hongyi, Khader, Firas, Truhn, Daniel, Aimyshev, Temirgali, Zhanibekuly, Yerkebulan, Le, Tien-Dung, Galdran, Adrian, Ballester, Miguel Ángel González, Carneiro, Gustavo, G, Devika R, S, Hrishikesh P, Puthussery, Densen, Liu, Hong, Yang, Zekang, Kondo, Satoshi, Kasai, Satoshi, Wang, Edward, Durvasula, Ashritha, Heras, Jónathan, Zapata, Miguel Ángel, Araújo, Teresa, Aresta, Guilherme, Bogunović, Hrvoje, Arikan, Mustafa, Lee, Yeong Chan, Cho, Hyun Bin, Choi, Yoon Ho, Qayyum, Abdul, Razzak, Imran, van Ginneken, Bram, Lemij, Hans G., Sánchez, Clara I.
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models f
Externí odkaz:
http://arxiv.org/abs/2302.01738
We study the impact of different loss functions on lesion segmentation from medical images. Although the Cross-Entropy (CE) loss is the most popular option when dealing with natural images, for biomedical image segmentation the soft Dice loss is ofte
Externí odkaz:
http://arxiv.org/abs/2209.06078
Autor:
Galdran, Adrian, Hewitt, Katherine J., Ghaffari, Narmin L., Kather, Jakob N., Carneiro, Gustavo, Ballester, Miguel A. González
Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to address this problem by applying Open Set Recognition techniques to the ta
Externí odkaz:
http://arxiv.org/abs/2206.10033
Autor:
Urru, Andrea, Nakaki, Ayako, Benkarim, Oualid, Crovetto, Francesca, Segales, Laura, Comte, Valentin, Hahner, Nadine, Eixarch, Elisenda, Gratacós, Eduard, Crispi, Fàtima, Piella, Gemma, Ballester, Miguel A González
The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a l
Externí odkaz:
http://arxiv.org/abs/2205.07575
Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reason
Externí odkaz:
http://arxiv.org/abs/2203.10417