Search Results for author: Aleksandar Vakanski

Found 20 papers, 4 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

Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials

no code implementations28 Sep 2023 Mohammad Karimzadeh, Aleksandar Vakanski, Fei Xu, Xinchang Zhang

Additive manufacturing has revolutionized the manufacturing of complex parts by enabling direct material joining and offers several advantages such as cost-effective manufacturing of complex parts, reducing manufacturing waste, and opening new possibilities for manufacturing automation.

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

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

no code implementations28 Jan 2023 Fadi Alharbi, Aleksandar Vakanski

Furthermore, reviewed are pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples.

Feature Engineering

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

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

Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection

no code implementations14 Jun 2021 Sujata Butte, Aleksandar Vakanski, Kasia Duellman, Haotian Wang, Amin Mirkouei

Recent research on the application of remote sensing and deep learning-based analysis in precision agriculture demonstrated a potential for improved crop management and reduced environmental impacts of agricultural production.

Management object-detection +1

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

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

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

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.

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

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