no code implementations • 20 Apr 2017 • Milad Zafar Nezhad, Dongxiao Zhu, Xiangrui Li, Kai Yang, Phillip Levy
In this paper, we propose a new deep feature selection method based on deep architecture.
no code implementations • 26 Sep 2017 • Milad Zafar Nezhad, Dongxiao Zhu, Najibesadat Sadati, Kai Yang, Phillip Levy
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups.
no code implementations • 6 Jan 2018 • Najibesadat Sadati, Milad Zafar Nezhad, Ratna Babu Chinnam, Dongxiao Zhu
Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights in the choice of deep feature representation techniques.
no code implementations • 10 Apr 2018 • Milad Zafar Nezhad, Najibesadat Sadati, Kai Yang, Dongxiao Zhu
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare.
no code implementations • 27 Dec 2018 • Mohammad Hessam Olya, Dongxiao Zhu, Kai Yang
This issue becomes more critical for the healthcare facilities that provide service for chronic disease treatment because of the need for continuous treatments over the time.
no code implementations • 9 Jun 2019 • Xiangrui Li, Jasmine Hect, Moriah Thomason, Dongxiao Zhu
The findings demonstrate that deep CNNs are a promising approach for identifying spontaneous functional patterns in fetal brain activity that discriminate age groups.
no code implementations • 10 Jun 2019 • Xiangrui Li, Dongxiao Zhu
Classification on imbalanced datasets is a challenging task in real-world applications.
no code implementations • MIDL 2019 • Xin Li, Rui Cao, Dongxiao Zhu
Medical imaging contains the essential information for rendering diagnostic and treatment decisions.
no code implementations • 29 Jul 2019 • Lu Wang, Dongxiao Zhu
Many real-world datasets are labeled with natural orders, i. e., ordinal labels.
no code implementations • 24 Aug 2019 • Najibesadat Sadati, Milad Zafar Nezhad, Ratna Babu Chinnam, Dongxiao Zhu
Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights in the choice of deep feature representation techniques.
no code implementations • 23 Feb 2020 • Xiangrui Li, Deng Pan, Xin Li, Dongxiao Zhu
In each iteration of SGD, a mini-batch from the training data is sampled and the true gradient of the loss function is estimated as the noisy gradient calculated on this mini-batch.
1 code implementation • 4 Mar 2020 • Xiangrui Li, Xin Li, Deng Pan, Dongxiao Zhu
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision.
3 code implementations • 22 Mar 2020 • Yao Qiang, Xin Li, Dongxiao Zhu
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • 6 Apr 2020 • Xin Li, Chengyin Li, Dongxiao Zhu
We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network to perform on-device COVID-19 patient triage and follow-up.
1 code implementation • 24 Jun 2020 • Xin Li, Deng Pan, Dongxiao Zhu
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems.
no code implementations • 12 Jul 2020 • Deng Pan, Xiangrui Li, Xin Li, Dongxiao Zhu
Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items.
1 code implementation • 14 Dec 2020 • Xin Li, Xiangrui Li, Deng Pan, Dongxiao Zhu
This inspires us to propose a new Probabilistically Compact (PC) loss with logit constraints which can be used as a drop-in replacement for cross-entropy (CE) loss to improve CNN's adversarial robustness.
no code implementations • 10 Jan 2021 • Yao Qiang, Supriya Tumkur Suresh Kumar, Marco Brocanelli, Dongxiao Zhu
On-device Deep Neural Networks (DNNs) have recently gained more attention due to the increasing computing power of the mobile devices and the number of applications in Computer Vision (CV), Natural Language Processing (NLP), and Internet of Things (IoTs).
no code implementations • 9 Sep 2022 • Xin Li, Yao Qiang, Chengyin Li, Sijia Liu, Dongxiao Zhu
We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets.
1 code implementation • 6 Oct 2022 • Chengyin Li, Yao Qiang, Rafi Ibn Sultan, Hassan Bagher-Ebadian, Prashant Khanduri, Indrin J. Chetty, Dongxiao Zhu
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-based models in capturing long-range global context.
1 code implementation • 23 Oct 2022 • Chengyin Li, Zheng Dong, Nathan Fisher, Dongxiao Zhu
Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers.
no code implementations • 23 Nov 2022 • Xin Li, Xiangrui Li, Deng Pan, Yao Qiang, Dongxiao Zhu
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i. e., last hidden layer) and a linear classifier (i. e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e. g., cross-entropy).
1 code implementation • 17 Jan 2023 • Xin Li, Deng Pan, Chengyin Li, Yao Qiang, Dongxiao Zhu
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns.
no code implementations • 31 Jan 2023 • Yao Qiang, Chengyin Li, Prashant Khanduri, Dongxiao Zhu
Importantly, our DSA framework leads to improved fairness guarantees over prior works on multiple prediction tasks without compromising target prediction performance.
no code implementations • 28 Aug 2023 • Chengyin Li, Prashant Khanduri, Yao Qiang, Rafi Ibn Sultan, Indrin Chetty, Dongxiao Zhu
In addition to the domain gaps between natural and medical images, disparities in the spatial arrangement between 2D and 3D images, the substantial computational burden imposed by powerful GPU servers, and the time-consuming manual prompt generation impede the extension of SAM to a broader spectrum of medical image segmentation applications.
1 code implementation • 14 Sep 2023 • Yao Qiang, Chengyin Li, Prashant Khanduri, Dongxiao Zhu
Furthermore, if ViTs are not properly trained with the given data and do not prioritize the region of interest, the {\it post hoc} methods would be less effective.
no code implementations • 9 Oct 2023 • Mohammad Peivandi, Jason Zhang, Michael Lu, Dongxiao Zhu, Zhifeng Kou
In our evaluation, we compared this improved model to two benchmarks: the pretrained SAM and the widely used model, nnUNetv2.
1 code implementation • 16 Nov 2023 • Yao Qiang, Xiangyu Zhou, Dongxiao Zhu
In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific tasks by utilizing labeled examples as demonstrations in the precondition prompts.
1 code implementation • 19 Nov 2023 • Rafi Ibn Sultan, Chengyin Li, Hui Zhu, Prashant Khanduri, Marco Brocanelli, Dongxiao Zhu
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation.
no code implementations • 21 Nov 2023 • Prashant Khanduri, Chengyin Li, Rafi Ibn Sultan, Yao Qiang, Joerg Kliewer, Dongxiao Zhu
A key novelty of our work is to develop solution accuracy-independent algorithms that do not require large batch gradients (and function evaluations) for solving federated CO problems.
1 code implementation • 21 Dec 2023 • Zhiyu Zhu, Huaming Chen, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Minhui Xue, Dongxiao Zhu, Kim-Kwang Raymond Choo
To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome.
1 code implementation • 21 Feb 2024 • Yao Qiang, Xiangyu Zhou, Saleh Zare Zade, Mohammad Amin Roshani, Douglas Zytko, Dongxiao Zhu
The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities.