Search Results for author: Min Xian

Found 34 papers, 7 papers with code

A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation

no code implementations22 Mar 2024 Kyle Lucke, Aleksandar Vakanski, Min Xian

In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist.

Anatomy Semantic Segmentation

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks

1 code implementation4 Nov 2023 Longze li, Jiang Chang, Aleksandar Vakanski, Yachun Wang, Tiankai Yao, Min Xian

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated.

Active Learning Decision Making +4

Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network

no code implementations4 Aug 2023 Bryar Shareef, Min Xian, Aleksandar Vakanski, Haotian Wang

Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations.

Classification Image Classification

Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model

1 code implementation16 May 2023 Fenghe Tang, Jianrui Ding, Lingtao Wang, Min Xian, Chunping Ning

Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.

Image Segmentation Medical Image Segmentation +2

CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

no code implementations9 Mar 2023 Shoukun Sun, Min Xian, Fei Xu, Luca Capriotti, Tiankai Yao

Remarkably, our model reduces by 33. 2\%, and 15. 5\% the number of clicks required to surpass an IoU of 0. 95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively.

Image Augmentation Image Segmentation +3

Enhanced Sharp-GAN For Histopathology Image Synthesis

no code implementations24 Jan 2023 Sujata Butte, Haotian Wang, Aleksandar Vakanski, Min Xian

To address the challenges, we propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization.

Image Generation Segmentation

CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

2 code implementations24 Oct 2022 Fenghe Tang, Lingtao Wang, Chunping Ning, Min Xian, Jianrui Ding

However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information.

Image Segmentation Medical Image Segmentation +2

SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis

no code implementations2 Sep 2022 Haotian Wang, Min Xian, Aleksandar Vakanski, Bryar Shareef

Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered nuclei.

Image Generation Instance Segmentation +2

MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer

no code implementations2 May 2022 Shoukun Sun, Min Xian, Aleksandar Vakanski, Hossny Ghanem

Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models' generalizability.

Adversarial Robustness Image Classification +1

Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis

no code implementations27 Oct 2021 Sujata Butte, Haotian Wang, Min Xian, Aleksandar Vakanski

Conditional generative adversarial networks have been applied to generate synthetic histopathology images to alleviate this issue, but current approaches fail to generate clear contours for overlapped and touching nuclei.

Generative Adversarial Network Image Generation

BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images

no code implementations5 Oct 2021 Boyu Zhang, Aleksandar Vakanski, Min Xian

In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians.

Decision Making

Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis

1 code implementation4 Mar 2021 Aleksandar Vakanski, Min Xian

The generalization performance of deep learning models for medical image analysis often decreases on images collected with different devices for data acquisition, device settings, or patient population.

Domain Generalization Generalization Bounds

Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation

no code implementations24 Feb 2021 Changfa Shi, Min Xian, Xiancheng Zhou, Haotian Wang, Heng-Da Cheng

Both qualitative and quantitative results demonstrate that, in the presence of major pathology, the proposed method is more accurate and robust than state-of-the-art methods.

Image Registration Liver Segmentation +2

ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation

no code implementations27 Sep 2020 Bryar Shareef, Alex Vakanski, Min Xian, Phoebe E. Freer

Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification.

Anatomy Breast Cancer Detection +2

A Review of Computational Approaches for Evaluation of Rehabilitation Exercises

no code implementations29 Feb 2020 Yalin Liao, Aleksandar Vakanski, Min Xian, David Paul, Russell Baker

The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems.

BIG-bench Machine Learning Feature Engineering

Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images

no code implementations3 Feb 2020 Haotian Wang, Min Xian, Aleksandar Vakanski

Separating overlapped nuclei is a major challenge in histopathology image analysis.

Stan: Small tumor-aware network for breast ultrasound image segmentation

3 code implementations3 Feb 2020 Bryar Shareef, Min Xian, Aleksandar Vakanski

The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.

Image Segmentation Tumor Segmentation

Breast Anatomy Enriched Tumor Saliency Estimation

no code implementations23 Oct 2019 Fei Xu, Yingtao Zhang, Min Xian, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang

Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers.

Anatomy Saliency Prediction

Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

1 code implementation20 Oct 2019 Aleksandar Vakanski, Min Xian, Phoebe Freer

The salient attention model has potential to enhance accuracy and robustness in processing medical images of other organs, by providing a means to incorporate task-specific knowledge into deep learning architectures.

Lesion Segmentation Segmentation +1

Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling

no code implementations18 Jun 2019 Fei Xu, Yingtao Zhang, Min Xian, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang

First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues.

Anatomy Feature Correlation +1

A Hybrid Framework for Tumor Saliency Estimation

no code implementations27 Jun 2018 Fei Xu, Min Xian, Yingtao Zhang, Kuan Huang, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang

Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality.

Saliency Prediction Segmentation +1

Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices

no code implementations27 Jun 2018 Yusheng Luo, Min Xian, Manish Mohanpurkar, Bishnu P. Bhattarai, Anudeep Medam, Rahul Kadavil, Rob Hovsapian

Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption.

Scheduling

Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey

no code implementations13 Feb 2018 Boyu Zhang, Yingtao Zhang, H. D. Cheng, Min Xian, Shan Gai, Olivia Cheng, Kuan Huang

In this survey paper, we classify the existing methods by their principles and discuss the current research status and point out the future research trend in-depth.

Image Segmentation MRI segmentation +2

BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

1 code implementation9 Jan 2018 Min Xian, Yingtao Zhang, H. D. Cheng, Fei Xu, Kuan Huang, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems.

Image Segmentation Segmentation +1

Neutro-Connectedness Cut

no code implementations19 Dec 2015 Min Xian, Yingtao Zhang, H. D. Cheng, Fei Xu, Jianrui Ding

Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches.

Image Segmentation Interactive Segmentation +2

LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes

no code implementations11 Jul 2015 Hongkai Yu, Youjie Zhou, Hui Qian, Min Xian, Yuewei Lin, Dazhou Guo, Kang Zheng, Kareem Abdelfatah, Song Wang

In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object.

Image Segmentation Object +5

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