Zobrazeno 1 - 10
of 307
pro vyhledávání: '"Dat T. Tô"'
Autor:
Nguyen, Huy T., Lam, Thinh B., Tran, Quan D. D., Nguyen, Minh T., Chung, Dat T., Dinh, Vinh Q.
This paper investigates the impact of breast density distribution on the generalization performance of deep-learning models on mammography images using the VinDr-Mammo dataset. We explore the use of domain adaptation techniques, specifically Domain A
Externí odkaz:
http://arxiv.org/abs/2306.06893
Publikováno v:
Communications in Mathematics, Volume 31 (2023), Issue 2 (Special issue: Euclidean lattices: theory and applications) (October 18, 2023) cm:11138
In this paper, we find criteria for when cyclic cubic and cyclic quartic fields have well-rounded ideal lattices. We show that every cyclic cubic field has at least one well-rounded ideal. We also prove that there exist families of cyclic quartic fie
Externí odkaz:
http://arxiv.org/abs/2303.16968
Publikováno v:
Journal of Algebra and Its Applications, Vol. 21, No. 07, 2250133 (2022)
In this paper, we investigate the properties of well-rounded twists of a given ideal lattice of an imaginary quadratic field $K$. We show that every ideal lattice $I$ of $K$ has at least one well-rounded twist lattice. Moreover, we provide an explici
Externí odkaz:
http://arxiv.org/abs/2210.15049
Autor:
Ngo, Dat T., Nguyen, Thao T. B., Nguyen, Hieu T., Nguyen, Dung B., Nguyen, Ha Q., Pham, Hieu H.
The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical
Externí odkaz:
http://arxiv.org/abs/2208.03403
Publikováno v:
In Journal of Medical Imaging and Radiation Sciences January 2025 56(1)
Autor:
Ma, Lin, Chi, Weicheng, Morgan, Howard E., Lin, Mu-Han, Chen, Mingli, Sher, David, Moon, Dominic, Vo, Dat T., Avkshtol, Vladimir, Lu, Weiguo, Gu, Xuejun
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such
Externí odkaz:
http://arxiv.org/abs/2108.08731
Publikováno v:
In Journal of Organometallic Chemistry 15 May 2024 1012
Publikováno v:
In Advances in Space Research 1 February 2024 73(3):1630-1645
Autor:
Nguyen, Ha Q., Lam, Khanh, Le, Linh T., Pham, Hieu H., Tran, Dat Q., Nguyen, Dung B., Le, Dung D., Pham, Chi M., Tong, Hang T. T., Dinh, Diep H., Do, Cuong D., Doan, Luu T., Nguyen, Cuong N., Nguyen, Binh T., Nguyen, Que V., Hoang, Au D., Phan, Hien N., Nguyen, Anh T., Ho, Phuong H., Ngo, Dat T., Nguyen, Nghia T., Nguyen, Nhan T., Dao, Minh, Vu, Van
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormaliti
Externí odkaz:
http://arxiv.org/abs/2012.15029
The chest X-rays (CXRs) is one of the views most commonly ordered by radiologists (NHS),which is critical for diagnosis of many different thoracic diseases. Accurately detecting thepresence of multiple diseases from CXRs is still a challenging task.
Externí odkaz:
http://arxiv.org/abs/2005.12734