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pro vyhledávání: '"Morales-Alvarez A"'
Autor:
Castro-Macías, Francisco M., Morales-Álvarez, Pablo, Wu, Yunan, Molina, Rafael, Katsaggelos, Aggelos K.
Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically seeks predictions at both bag and instance levels (classification and loc
Externí odkaz:
http://arxiv.org/abs/2410.03276
From SAE Level 3 of automation onwards, drivers are allowed to engage in activities that are not directly related to driving during their travel. However, in level 3, a misunderstanding of the capabilities of the system might lead drivers to engage i
Externí odkaz:
http://arxiv.org/abs/2408.09833
Autor:
Schmidt, Arne, Morales-Álvarez, Pablo, Cooper, Lee A. D., Newberg, Lee A., Enquobahrie, Andinet, Katsaggelos, Aggelos K., Molina, Rafael
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambigui
Externí odkaz:
http://arxiv.org/abs/2404.04663
Publikováno v:
Journal: Artificial Intelligence, Pages: 104115, Publisher: Elsevier, Year: 2024
Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas and is particularly well suited to medical imaging. Probabilistic MIL methods, and more specifically Gaussian Proce
Externí odkaz:
http://arxiv.org/abs/2403.14829
Autor:
Certad, Novel, del Re, Enrico, Korndörfer, Helena, Schröder, Gregory, Morales-Alvarez, Walter, Tschernuth, Sebastian, Gankhuyag, Delgermaa, del Re, Luigi, Olaverri-Monreal, Cristina
The acquisition and analysis of high-quality sensor data constitute an essential requirement in shaping the development of fully autonomous driving systems. This process is indispensable for enhancing road safety and ensuring the effectiveness of the
Externí odkaz:
http://arxiv.org/abs/2403.08455
In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has become very popular in many different areas. A paradigmatic example is computational pathology, where the lack of patch-level labels for whole-slide images prev
Externí odkaz:
http://arxiv.org/abs/2310.19359
Autor:
Morales-Alvarez, Walter, Certad, Novel, Roitberg, Alina, Stiefelhagen, Rainer, Olaverri-Monreal, Cristina
For driver observation frameworks, clean datasets collected in controlled simulated environments often serve as the initial training ground. Yet, when deployed under real driving conditions, such simulator-trained models quickly face the problem of d
Externí odkaz:
http://arxiv.org/abs/2307.16543
Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer variation NetwO
Externí odkaz:
http://arxiv.org/abs/2307.11397
Autor:
Wu, Yunan, Castro-Macías, Francisco M., Morales-Álvarez, Pablo, Molina, Rafael, Katsaggelos, Aggelos K.
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assumes that instances in each bag are independent samples from a given dis
Externí odkaz:
http://arxiv.org/abs/2307.09457
Publikováno v:
Computer Aided Systems Theory EUROCAST 2022
This paper presents the development of the JKU-ITS Last Mile Delivery Robot. The proposed approach utilizes a combination of one 3D LIDAR, RGB-D camera, IMU and GPS sensor on top of a mobile robot slope mower. An embedded computer, running ROS1, is u
Externí odkaz:
http://arxiv.org/abs/2305.18276