Robust Cross-vendor Mammographic Texture Models Using Augmentation-based
Domain Adaptation for Long-term Breast Cancer Risk

v1v2 (latest)

Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk

27 December 2022

Andreas D. Lauritzen

M. V. von Euler-Chelpin

N. Karssemeijer

ArXiv (abs)PDF HTML

Papers citing "Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk"

8 / 8 papers shown

Title
Augmentation Strategies for Learning with Noisy Labels Kento Nishi Yi Ding Alex Rich Tobias Höllerer NoLa 65 118 0 03 Mar 2021
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models Yue Liu Hossein Azizpour Fredrik Strand Kevin Smith 35 11 0 11 Jul 2020
What shapes feature representations? Exploring datasets, architectures, and training Katherine L. Hermann Andrew Kyle Lampinen OOD 89 157 0 22 Jun 2020
Generalizing to Unseen Domains via Adversarial Data Augmentation Riccardo Volpi Hongseok Namkoong Ozan Sener John C. Duchi Vittorio Murino Silvio Savarese OOD 122 786 0 30 May 2018
Stabilizing Training of Generative Adversarial Networks through Regularization Kevin Roth Aurelien Lucchi Sebastian Nowozin Thomas Hofmann GAN 68 447 0 25 May 2017
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.3K 194,510 0 10 Dec 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,615 0 01 Sep 2014

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