Search Results for author: Dongxiao Zhu

Found 19 papers, 5 papers with code

Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System

no code implementations9 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.

Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification

no code implementations10 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).

Adversarial Robustness General Classification +4

Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints

1 code implementation14 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.

Adversarial Robustness

Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability

no code implementations12 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.

Collaborative Filtering Explainable Recommendation +1

Defending against adversarial attacks on medical imaging AI system, classification or detection?

1 code implementation24 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.

Adversarial Defense General Classification

COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

1 code implementation6 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.

Computed Tomography (CT) Trajectory Prediction +1

Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach

3 code implementations22 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 TAG

On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

1 code implementation4 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.

Classification General Classification +1

Improve SGD Training via Aligning Mini-batches

no code implementations23 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.

Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study

no code implementations24 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.

Representation Learning Small Data Image Classification

Vispi: Automatic Visual Perception and Interpretation of Chest X-rays

no code implementations MIDL 2019 Xin Li, Rui Cao, Dongxiao Zhu

Medical imaging contains the essential information for rendering diagnostic and treatment decisions.

Image Captioning

Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks

no code implementations9 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.

Multi-task Prediction of Patient Workload

no code implementations27 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.

Decision Making Multi-Task Learning

Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study

no code implementations6 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.

Representation Learning Small Data Image Classification

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

no code implementations26 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.

SAFS: A Deep Feature Selection Approach for Precision Medicine

no code implementations20 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.

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