no code implementations • 22 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.
1 code implementation • 4 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.
no code implementations • 28 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.
no code implementations • 27 Aug 2023 • Mohammad Karimzadeh, Aleksandar Vakanski, Min Xian, Boyu Zhang
Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women.
no code implementations • 4 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.
no code implementations • 28 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.
no code implementations • 24 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.
no code implementations • 2 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.
no code implementations • 2 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.
no code implementations • 13 Jan 2022 • Jiaqiao Shi, Aleksandar Vakanski, Min Xian, Jianrui Ding, Chunping Ning
The accuracy, sensitivity, and specificity of tumor classification is 88. 6%, 94. 1%, and 85. 3%, respectively.
no code implementations • 27 Oct 2021 • Haotian Wang, Min Xian, Aleksandar Vakanski
The proposed topology loss computes gland topology using gland skeletons and markers.
no code implementations • 27 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.
no code implementations • 5 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.
no code implementations • 30 Sep 2021 • Haotian Wang, Aleksandar Vakanski, Changfa Shi, Min Xian
Separating overlapped nuclei is a major challenge in histopathology image analysis.
no code implementations • 14 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.
1 code implementation • 4 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.
no code implementations • 29 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.
no code implementations • 3 Feb 2020 • Haotian Wang, Min Xian, Aleksandar Vakanski
Separating overlapped nuclei is a major challenge in histopathology image analysis.
3 code implementations • 3 Feb 2020 • Bryar Shareef, Min Xian, Aleksandar Vakanski
The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
1 code implementation • 20 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.
Ranked #1 on Tumor Segmentation on BUS 2017 Dataset B