Publications

Dynamic linear transformer for 3D biomedical image segmentation

Zhang, Z., & Bagci, U.

Isler, Ilkin, et al.

Multi-contrast MRI segmentation trained on synthetic images

Irmakci, I., Unel, Z. E., Ikizler-Cinbis, N., & Bagci, U.

Multi-institutional large-scale validation of 8 methods for automatic knee MRI segmentation for use in clinical trials

Dam, E. B., et al.

Musculoskeletal MR image segmentation with artificial intelligence

Keles, E., Irmakci, I., Bagci, U.

Neural transformers for intraductal papillary mucosal neoplasms (IPMN) classification in MRI images

Proietto Salanitri, F. et al.

Out of distribution detection, generalization, and robustness triangle with maximum probability theorem

Marvasti, Amir Emad, et al.

Tganet: text-guided attention for improved polyp segmentation

Tomar, N. K., Jha, D., Bagci, U., & Ali, S.

Şenol Çomoğlu, Sinan Öztürk, Ahmet Topçu, Fatma Kulalı, Aydın Kant, Resul Sobay, Mustafa Arslan, Hanife Şeyda Ülgür, Uğur Kostakoğlu, Eyüp Veli Küçük, Hanife Nur Karakoç, Merve Çağlar, Gülsüm Uzuğ, Ulas Bagci , Ömer Faruk Özkan, Gürdal Yılmaz

Çomoglu, S., Öztürk, S., Topçu, A., Kulali, F., Kant, A., Sobay, R., … & Yilmaz, G.

Transformer-based generative adversarial network for liver segmentation

Demir, Ugur, et al.

Video analytics in elite soccer: a distributed computing perspective

Jha, Debesh, et al.

Video capsule endoscopy classification using focal modulation guided convolutional neural network

Srivastava, Abhishek, et al.

A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics

He, B., Song, Y., Wang, L., et al.

A novel decision support system for long-term management of bridge networks

Karaaslan, E., Bagci, U., & Catbas, N

Attention-guided analysis of infrastructure damage with semi-supervised deep learning.

Karaaslan, E., Bagci, U., & Catbas, F. N.

Capsules for biomedical image segmentation

LaLonde, R., Xu, Z., Irmakci, I., Jain, S., & Bagci, U.

Deep learning based staging of bone lesions from computed tomography scans

Masoudi, S., Mehralivand, S., Harmon, S.A., et al.

Deep multi-stage model for automated landmarking of craniomaxillofacial CT scans

Palazzo, Simone, Giovanni Bellitto, Luca Prezzavento, et al.

Deep recurrent-convolutional model for automated segmentation of craniomaxillofacial CT scans.

Murabito, Francesca, Simone Palazzo, F. Proietto Salanitri, et al.