Li, Jing-Jing Collins, Anne GE
Learning structures that effectively abstract decision policies is key to the flexibility of human intelligence. Previous work has shown that humans use hierarchically structured policies to efficiently navigate complex and dynamic environments. However, the computational processes that support the learning and construction of such policies remain ...
Zubair Rahman, A M J Md Mythili, R Chokkanathan, K Mahesh, T R Vanitha, K Yimer, Temesgen Engida
Published in
BMC medical imaging
The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject to variability and may lack precision. Current methodologies leverage conventional deep learning models that...
Hopson, Jessica B. Flaus, Anthime McGinnity, Colm J. Neji, Radhouene Reader, Andrew J. Hammers, Alexander
Published in
IEEE transactions on radiation and plasma medical sciences
Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically-annotated medical images. Many two-dimensional pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (...
Saeidian, Jamshid Azimi, Hossein Azimi, Zohre Pouya, Parnia Asadigandomani, Hassan Riazi-Esfahani, Hamid Hayati, Alireza Daneshvar, Kimia Khalili Pour, Elias
Published in
BMC medical imaging
This study aimed to evaluate the effectiveness of DeepLabv3+with Squeeze-and-Excitation (DeepLabv3+SE) architectures for segmenting the choroid in optical coherence tomography (OCT) images of patients with diabetic retinopathy. A total of 300 B-scans were selected from 21 patients with mild to moderate diabetic retinopathy. Six DeepLabv3+SE variant...
Gérard, Mahaut Dubois, Guillaume Hanne-Poujade, Sandrine Mezghani, Neila Chateau, Henry
Kumari, Priyanka Guilherme, Madureira Sanches Ribeiro Choudhary, Pratyush Van Laethem, Thomas Fillet, Marianne Hubert, Philippe Sacre, Pierre-Yves Hubert, Cédric
peer reviewed / QSRR is a valuable technique for the retention time predictions of small molecules. This aims to bridge the gap between molecular structure and chromatographic behavior, offering invaluable insights for analytical chemistry. Given the challenge of simultaneous target prediction with variable experimental conditions and the scarcity ...
López-Zambrano, Javier Lara, Juan A Romero, Cristóbal
Published in
Journal of computing in higher education
One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level attribute...
Shima, Akihiro Ishitsuka, Kazuya Lin, Weiren Bjarkason, Elvar K. Suzuki, Anna
Deep learning has gained attention as a potentially powerful technique for modeling natural-state geothermal systems; however, its physical validity and prediction inaccuracy at extrapolation ranges are limiting. This study proposes the use of transfer learning in physics-informed neural networks to leverage prior expert knowledge at the target sit...
Alazwari, Sana Alsamri, Jamal Alamgeer, Mohammad Alotaibi, Saud S Obayya, Marwa Salama, Ahmed S
Published in
Scientific reports
The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with G...
Ban, Jakob
V diplomski nalogi sem obravnaval področje strojnega vida, ki omogoča računalnikom prepoznavo objektov iz slik in video posnetkov. Glede na to, da strojni vid spada pod področje umetne inteligence, sem opisal tudi delovanje strojnega učenja in nevronskih mrež. Glavni cilj naloge je bil izdelati aplikacijo, ki bi s strojnim vidom prepoznavala vrste ...