Zobrazeno 1 - 10
of 658
pro vyhledávání: '"Tan, Jeremy"'
Autor:
Müller, Andre Matthias, Wang, Nan Xin, Yao, Jiali, Tan, Chuen Seng, Low, Ivan Cherh Chiet, Lim, Nicole, Tan, Jeremy, Tan, Agnes, Müller-Riemenschneider, Falk
Publikováno v:
JMIR mHealth and uHealth, Vol 7, Iss 10, p e14120 (2019)
BackgroundWrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of HR data obtained from such trackers has not been thoroughly assessed ou
Externí odkaz:
https://doaj.org/article/ea9523c483f643c7b7240e3b76f69b1c
Autor:
Baugh, Matthew, Tan, Jeremy, Müller, Johanna P., Dombrowski, Mischa, Batten, James, Kainz, Bernhard
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training. The long tail of infinitely many out-of-distribution
Externí odkaz:
http://arxiv.org/abs/2307.00899
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class of normative samples, e.g., through epistemic uncertainty estimates,
Externí odkaz:
http://arxiv.org/abs/2303.13227
Autor:
Lebbos, Clara, Barcroft, Jen, Tan, Jeremy, Muller, Johanna P., Baugh, Matthew, Vlontzos, Athanasios, Saso, Srdjan, Kainz, Bernhard
Publikováno v:
ASMUS 2022, LNCS 13565, p. 106, 2022
Ovarian cancer is the most lethal gynaecological malignancy. The disease is most commonly asymptomatic at its early stages and its diagnosis relies on expert evaluation of transvaginal ultrasound images. Ultrasound is the first-line imaging modality
Externí odkaz:
http://arxiv.org/abs/2209.12305
The wide variety of in-distribution and out-of-distribution data in medical imaging makes universal anomaly detection a challenging task. Recently a number of self-supervised methods have been developed that train end-to-end models on healthy data au
Externí odkaz:
http://arxiv.org/abs/2209.01124
Autor:
Tan, Jeremy
The Zarankiewicz function gives, for a chosen matrix and minor size, the maximum number of ones in a binary matrix not containing an all-one minor. Tables of this function for small arguments have been compiled, but errors are known in them. We both
Externí odkaz:
http://arxiv.org/abs/2203.02283
Publikováno v:
In Canadian Journal of Ophthalmology/Journal canadien d'ophtalmologie October 2024 59(5):311-323
Publikováno v:
In Ophthalmology Science January-February 2025 5(1)
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data. NSA integrates Poisson image editing to seamlessly
Externí odkaz:
http://arxiv.org/abs/2109.15222
Publikováno v:
In Ophthalmology June 2024 131(6):658-666