13 citations · 29 across the 9 of their papers we have counts for
13 papers
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…