no code implementations • 27 Mar 2024 • Mohammad R. Salmanpour, Amin Mousavi, Yixi Xu, William B Weeks, Ilker Hacihaliloglu
Finally, a detailed qualitative assessment by five medical doctors indicated a lack of low level feature discovery in image to image translation tasks.
no code implementations • 11 Mar 2024 • Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O, Carlos F. Uribe, Rahul Dodhia, Juan L. Ferres, Arman Rahmim
Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG).
1 code implementation • 16 Nov 2023 • Shadab Ahamed, Yixi Xu, Claire Gowdy, Joo H. O, Ingrid Bloise, Don Wilson, Patrick Martineau, François Bénard, Fereshteh Yousefirizi, Rahul Dodhia, Juan M. Lavista, William B. Weeks, Carlos F. Uribe, Arman Rahmim
This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images.
no code implementations • 11 Sep 2020 • Yixi Xu, Sumit Mukherjee, Xiyang Liu, Shruti Tople, Rahul Dodhia, Juan Lavista Ferres
In this work, we propose the first formal framework for membership privacy estimation in generative models.
1 code implementation • 31 Dec 2019 • Sumit Mukherjee, Yixi Xu, Anusua Trivedi, Juan Lavista Ferres
It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared.
no code implementations • NeurIPS 2018 • Yixi Xu, Xiao Wang
This paper presents a general framework for norm-based capacity control for $L_{p, q}$ weight normalized deep neural networks.
no code implementations • 6 Apr 2017 • Yixi Xu, Jean Honorio, Xiao Wang
In this paper, we propose a compositional nonparametric method in which a model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is either a summation, a multiplication, or the application of one of the $q$ basis functions to one of the $p$ covariates.