Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Mann, Ritse"'
Autor:
Han, Luyi, Tan, Tao, Zhang, Tianyu, Wang, Xin, Gao, Yuan, Lu, Chunyao, Liang, Xinglong, Dou, Haoran, Huang, Yunzhi, Mann, Ritse
Adversarial learning helps generative models translate MRI from source to target sequence when lacking paired samples. However, implementing MRI synthesis with adversarial learning in clinical settings is challenging due to training instability and m
Externí odkaz:
http://arxiv.org/abs/2407.02911
Autor:
Garrucho, Lidia, Reidel, Claire-Anne, Kushibar, Kaisar, Joshi, Smriti, Osuala, Richard, Tsirikoglou, Apostolia, Bobowicz, Maciej, del Riego, Javier, Catanese, Alessandro, Gwoździewicz, Katarzyna, Cosaka, Maria-Laura, Abo-Elhoda, Pasant M., Tantawy, Sara W., Sakrana, Shorouq S., Shawky-Abdelfatah, Norhan O., Abdo-Salem, Amr Muhammad, Kozana, Androniki, Divjak, Eugen, Ivanac, Gordana, Nikiforaki, Katerina, Klontzas, Michail E., García-Dosdá, Rosa, Gulsun-Akpinar, Meltem, Lafcı, Oğuz, Mann, Ritse, Martín-Isla, Carlos, Prior, Fred, Marias, Kostas, Starmans, Martijn P. A., Strand, Fredrik, Díaz, Oliver, Igual, Laura, Lekadir, Karim
Current research in breast cancer Magnetic Resonance Imaging (MRI), especially with Artificial Intelligence (AI), faces challenges due to the lack of expert segmentations. To address this, we introduce the MAMA-MIA dataset, comprising 1506 multi-cent
Externí odkaz:
http://arxiv.org/abs/2406.13844
Autor:
Moriakov, Nikita, Peters, Jim, Mann, Ritse, Karssemeijer, Nico, van Dijck, Jos, Broeders, Mireille, Teuwen, Jonas
Lesion volume is an important predictor for prognosis in breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammograms, which
Externí odkaz:
http://arxiv.org/abs/2308.14369
Autor:
Wang, Xin, Tan, Tao, Gao, Yuan, Han, Luyi, Zhang, Tianyu, Lu, Chunyao, Beets-Tan, Regina, Su, Ruisheng, Mann, Ritse
Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalit
Externí odkaz:
http://arxiv.org/abs/2307.02935
Autor:
Zhang, Tianyu, Han, Luyi, D'Angelo, Anna, Wang, Xin, Gao, Yuan, Lu, Chunyao, Teuwen, Jonas, Beets-Tan, Regina, Tan, Tao, Mann, Ritse
Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue, and ha
Externí odkaz:
http://arxiv.org/abs/2307.00895
Autor:
Han, Luyi, Zhang, Tianyu, Huang, Yunzhi, Dou, Haoran, Wang, Xin, Gao, Yuan, Lu, Chunyao, Tao, Tan, Mann, Ritse
Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-
Externí odkaz:
http://arxiv.org/abs/2307.00885
Autor:
Dou, Haoran, Bi, Ning, Han, Luyi, Huang, Yuhao, Mann, Ritse, Yang, Xin, Ni, Dong, Ravikumar, Nishant, Frangi, Alejandro F., Huang, Yunzhi
Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the d
Externí odkaz:
http://arxiv.org/abs/2306.14687
Autor:
Zhang, Tianyu, Tan, Tao, Han, Luyi, Wang, Xin, Gao, Yuan, Teuwen, Jonas, Beets-Tan, Regina, Mann, Ritse
Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of lesions. Such multi-parameter MRI information has been shown to improve r
Externí odkaz:
http://arxiv.org/abs/2302.01788
Autor:
Han, Luyi, Tan, Tao, Zhang, Tianyu, Huang, Yunzhi, Wang, Xin, Gao, Yuan, Teuwen, Jonas, Mann, Ritse
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across sequences, which interferes with mining efficient representations by
Externí odkaz:
http://arxiv.org/abs/2302.00517
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestib
Externí odkaz:
http://arxiv.org/abs/2210.04255