Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Nascimento, Jacinto C."'
Autor:
Hsieh, Chihcheng, Moreira, Catarina, Nobre, Isabel Blanco, Sousa, Sandra Costa, Ouyang, Chun, Brereton, Margot, Jorge, Joaquim, Nascimento, Jacinto C.
X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating comprehensive clinical features and data i
Externí odkaz:
http://arxiv.org/abs/2407.08227
We present, for the first time, a novel theoretical approach to address the problem of correspondence free multivector cloud registration in conformal geometric algebra. Such formalism achieves several favorable properties. Primarily, it forms an ort
Externí odkaz:
http://arxiv.org/abs/2406.11732
Autor:
Duarte, Alexandre, Fernandes, Francisco, Pereira, João M., Moreira, Catarina, Nascimento, Jacinto C., Jorge, Joaquim
Publikováno v:
Journal of Real-Time Image Processing 2024
Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems. However, they require vast amounts of ground
Externí odkaz:
http://arxiv.org/abs/2406.03388
Autor:
Araújo, Diogo J., Verdelho, M. Rita, Bissoto, Alceu, Nascimento, Jacinto C., Santiago, Carlos, Barata, Catarina
Deep learning models have revolutionized the field of medical image analysis, due to their outstanding performances. However, they are sensitive to spurious correlations, often taking advantage of dataset bias to improve results for in-domain data, b
Externí odkaz:
http://arxiv.org/abs/2405.01654
Head pose estimation has become a crucial area of research in computer vision given its usefulness in a wide range of applications, including robotics, surveillance, or driver attention monitoring. One of the most difficult challenges in this field i
Externí odkaz:
http://arxiv.org/abs/2403.20251
Publikováno v:
Pattern Recognition, Volume 137, May 2023
Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for ma
Externí odkaz:
http://arxiv.org/abs/2311.06038
Autor:
Hsieh, Chihcheng, Nobre, Isabel Blanco, Sousa, Sandra Costa, Ouyang, Chun, Brereton, Margot, Nascimento, Jacinto C., Jorge, Joaquim, Moreira, Catarina
This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray i
Externí odkaz:
http://arxiv.org/abs/2302.13390
Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of survival models is censored data. Most of the current survival prediction
Externí odkaz:
http://arxiv.org/abs/2205.13226
Publikováno v:
In Computerized Medical Imaging and Graphics July 2024 115
Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that requi
Externí odkaz:
http://arxiv.org/abs/2102.10765