245 citations · 870 across the 16 of their papers we have counts for
17 papers
Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data
Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski +4
The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnost…
Dataset of Tensile Properties for Sub-sized Specimens of Nuclear Structural Materials
Longze Li, John W. Merickel, Yalei Tang +4
Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and ac…
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
Fadi Alharbi, Aleksandar Vakanski
Cancer is a term that denotes a group of diseases caused by abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO…
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer
Shoukun Sun, Min Xian, Aleksandar Vakanski +1
Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models' generalizability. However, RST and other…
EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer
Jiaqiao Shi, Aleksandar Vakanski, Min Xian +2
Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impede…
Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis
Sujata Butte, Haotian Wang, Min Xian +1
Existing deep learning-based approaches for histopathology image analysis require large annotated training sets to achieve good performance; but annotating histopathology images is…
TA-Net: Topology-Aware Network for Gland Segmentation
Haotian Wang, Min Xian, Aleksandar Vakanski
Gland segmentation is a critical step to quantitatively assess the morphology of glands in histopathology image analysis. However, it is challenging to separate densely clustered g…
BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images
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. This paper introduces BI-RADS-Net,…
Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection
Sujata Butte, Aleksandar Vakanski, Kasia Duellman +2
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 env…
Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis
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,…
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
Bryar Shareef, Alex Vakanski, Min Xian +1
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…
A Review of Computational Approaches for Evaluation of Rehabilitation Exercises
Yalin Liao, Aleksandar Vakanski, Min Xian +2
Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the hea…
Stan: Small tumor-aware network for breast ultrasound image segmentation
Bryar Shareef, Min Xian, Aleksandar Vakanski
Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been propose…
Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images
Haotian Wang, Min Xian, Aleksandar Vakanski
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on public datasets; ho…
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images
Aleksandar Vakanski, Min Xian, Phoebe Freer
Incorporating human domain knowledge for breast tumor diagnosis is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly acros…
A Deep Learning Framework for Assessing Physical Rehabilitation Exercises
Y. Liao, A. Vakanski, M. Xian
Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data…
Generative adversarial networks for generation and classification of physical rehabilitation movement episodes
L. Li, A. Vakanski
This article proposes a method for mathematical modeling of human movements related to patient exercise episodes performed during physical therapy sessions by using artificial neur…