Zobrazeno 1 - 10
of 5 799
pro vyhledávání: '"A Osmani"'
Internet of Things (IoT) devices generate heterogeneous data over time; and relying solely on individual data points is inadequate for accurate analysis. Segmentation is a common preprocessing step in many IoT applications, including IoT-based activi
Externí odkaz:
http://arxiv.org/abs/2404.11742
Medical image segmentation plays a vital role in various clinical applications, enabling accurate delineation and analysis of anatomical structures or pathological regions. Traditional CNNs have achieved remarkable success in this field. However, the
Externí odkaz:
http://arxiv.org/abs/2404.11361
In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch. Our work
Externí odkaz:
http://arxiv.org/abs/2404.03898
Autor:
Suvon, Mohammod N. I., Tripathi, Prasun C., Fan, Wenrui, Zhou, Shuo, Liu, Xianyuan, Alabed, Samer, Osmani, Venet, Swift, Andrew J., Chen, Chen, Lu, Haiping
Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these a
Externí odkaz:
http://arxiv.org/abs/2403.13658
Autor:
Fan, Wenrui, Suvon, Mohammod Naimul Islam, Zhou, Shuo, Liu, Xianyuan, Alabed, Samer, Osmani, Venet, Swift, Andrew, Chen, Chen, Lu, Haiping
Vision-language pre-training (VLP) models have shown significant advancements in the medical domain. Yet, most VLP models align raw reports to images at a very coarse level, without modeling fine-grained relationships between anatomical and pathologi
Externí odkaz:
http://arxiv.org/abs/2403.10635
Autor:
Hamidi, Massinissa, Osmani, Aomar
In this paper we will discuss metalearning and how we can go beyond the current classical learning paradigm. We will first address the importance of inductive biases in the learning process and what is at stake: the quantities of data necessary to le
Externí odkaz:
http://arxiv.org/abs/2401.00532
Autor:
Ciavolella, Giorgia, Granet, Julien, Goetz, Jacky, Osmani, Nael, Etchegaray, Christèle, Collin, Annabelle
The spread of metastases is a crucial process in which some questions remain unanswered. In this work, we focus on tumor cells circulating in the bloodstream, the so-called Circulating Tumor Cells (CTCs). Our aim is to characterize their trajectories
Externí odkaz:
http://arxiv.org/abs/2311.02091
Publikováno v:
BMC Women's Health, Vol 24, Iss 1, Pp 1-25 (2024)
Abstract Background This systematic literature review aims to summarize global research on parental acceptance, attitudes, and knowledge regarding human papillomavirus vaccinations. Methods The literature search was conducted in PubMed, Web of Scienc
Externí odkaz:
https://doaj.org/article/558f6d5900fc40bea793bad97d82a494
Manual segmentation of medical images (e.g., segmenting tumors in CT scans) is a high-effort task that can be accelerated with machine learning techniques. However, selecting the right segmentation approach depends on the evaluation function, particu
Externí odkaz:
http://arxiv.org/abs/2302.04135
Publikováno v:
Clinical and Investigative Orthodontics, Vol 83, Iss 3, Pp 97-105 (2024)
Background Although fixed orthodontic therapy offers numerous advantages, inadequate adherence to specific guidelines can lead to negative consequences, primarily affecting the gums. To enhance periodontal health, several surgical and non-surgical ap
Externí odkaz:
https://doaj.org/article/3430b6ef10524ec28ce2004359fc2a37