no code implementations • 28 Feb 2024 • Jiarui Xing, Nian Wu, Kenneth Bilchick, Frederick Epstein, Miaomiao Zhang
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images.
no code implementations • 5 Feb 2024 • Rugved Chavan, Gabriel Hyman, Zoraiz Qureshi, Nivetha Jayakumar, William Terrell, Stuart Berr, David Schiff, Megan Wardius, Nathan Fountain, Thomas Muttikkal, Mark Quigg, Miaomiao Zhang, Bijoy Kundu
These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections.
1 code implementation • 20 Dec 2023 • Tonmoy Hossain, Miaomiao Zhang
In this paper, we propose a novel model, Multimodal Geometric Augmentation (MGAug), that for the first time generates augmenting transformations in a multimodal latent space of geometric deformations.
no code implementations • 14 Dec 2023 • Lei Zhao, Miaomiao Zhang
This article mainly introduces how to use various basic emulators to form a combined emulator in the Jiutian Intelligence Network Simulation Platform to realize simulation service functions in different business scenarios.
no code implementations • 7 Nov 2023 • Lei Zhao, Miaomiao Zhang, Lv Zhe
JINSP(Jiutian Intelligence Network Simulation Platform) describes a series of basic emulators and their combinations, such as the simulation of the protocol stack for dynamic users in a real environment, which is composed of user behavior simulation, base station simulation, and terminal simulation.
no code implementations • 28 Sep 2023 • Lei Zhao, Miaomiao Zhang, Guangyu Li, Zhuowen Guan, Sijia Liu, Zhaobin Xiao, Yuting Cao, Zhe Lv, Yanping Liang
This paper introduced the JiuTian Intelligent Network Simulation Platform, which can provide wireless communication simulation data services for the Open Innovation Platform.
no code implementations • 6 Sep 2023 • Nivetha Jayakumar, Tonmoy Hossain, Miaomiao Zhang
In this paper, we propose a shape-aware network based on diffusion models for 3D image reconstruction, named SADIR, to address these issues.
no code implementations • 13 Mar 2023 • Nian Wu, Miaomiao Zhang
To achieve this, we develop a neural operator that for the first time learns the evolving trajectory of geodesic deformations parameterized in the tangent space of diffeomorphisms(a. k. a velocity fields).
no code implementations • 8 Mar 2023 • Jian Wang, Jiarui Xing, Jason Druzgal, William M. Wells III, Miaomiao Zhang
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i. e., caused by brain tumors).
no code implementations • 27 Feb 2023 • Tonmoy Hossain, Zoraiz Qureshi, Nivetha Jayakumar, Thomas Eluvathingal Muttikkal, Sohil Patel, David Schiff, Miaomiao Zhang, Bijoy Kundu
3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN.
no code implementations • 11 Nov 2022 • Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Frederick H. Epstein, Amit R. Patel, Miaomiao Zhang
With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification.
no code implementations • 11 Nov 2022 • Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Amit R. Patel, Miaomiao Zhang
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect.
1 code implementation • 25 Oct 2022 • Jian Wang, Miaomiao Zhang
We introduce a newly designed framework that (i) simultaneously derives features from both image and latent shape spaces with large intra-class variations; and (ii) gains increased model interpretability by allowing direct access to the underlying geometric features of image data.
1 code implementation • 3 Jan 2022 • Zihuan Qiu, Zhichuan Wang, Miaomiao Zhang, Ziyong Xu, Jie Fan, Linfeng Xu
However, due to the different sizes of polyps and the unclear boundary between polyps and their surrounding mucosa, it is challenging to segment polyps accurately.
Ranked #20 on Medical Image Segmentation on Kvasir-SEG
no code implementations • 13 Dec 2021 • Nian Wu, Jian Wang, Miaomiao Zhang, Guixu Zhang, Yaxin Peng, Chaomin Shen
Registration-based atlas building often poses computational challenges in high-dimensional image spaces.
1 code implementation • 12 Jul 2021 • Jian Wang, Miaomiao Zhang
This paper presents a novel hierarchical Bayesian model for unbiased atlas building with subject-specific regularizations of image registration.
no code implementations • 4 Mar 2021 • William Paul, Yinzhi Cao, Miaomiao Zhang, Phil Burlina
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic.
no code implementations • 28 Nov 2020 • Jian Wang, Miaomiao Zhang
This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration.
no code implementations • 17 Jul 2020 • Bai Xue, Miaomiao Zhang, Arvind Easwaran, Qin Li
In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning.
Systems and Control Formal Languages and Automata Theory Systems and Control
1 code implementation • CVPR 2020 • Jian Wang, Miaomiao Zhang
This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration.
1 code implementation • 23 Oct 2019 • Jie An, Mingshuai Chen, Bohua Zhan, Naijun Zhan, Miaomiao Zhang
We present an algorithm for active learning of deterministic timed automata with a single clock.
Formal Languages and Automata Theory
no code implementations • 3 Sep 2019 • Youshan Zhang, Jiarui Xing, Miaomiao Zhang
Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures.
no code implementations • 6 Mar 2019 • Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, Elfar Adalsteinsson, P. Ellen Grant, Polina Golland
To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.
no code implementations • 20 Mar 2018 • Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells III
Kernels of the GP are estimated by using variograms and a discrete grid search method.
no code implementations • 14 Mar 2018 • Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William M. Wells III, Sarah Frisken
For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
1 code implementation • 14 Aug 2016 • Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh, Tolga Tasdizen
We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning.
Electron Microscopy Image Segmentation Image Segmentation +2
no code implementations • 12 Aug 2016 • Ruizhi Liao, Esra Turk, Miaomiao Zhang, Jie Luo, Ellen Grant, Elfar Adalsteinsson, Polina Golland
We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series.
no code implementations • NeurIPS 2013 • Miaomiao Zhang, Tom Fletcher
Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA) for dimensionality reduction of data on a Riemannian manifold.