6 citations · 10 across the 5 of their papers we have counts for
6 papers
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…
Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images
Haotian Wang, Aleksandar Vakanski, Changfa Shi +1
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation…
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…
Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation
Changfa Shi, Min Xian, Xiancheng Zhou +2
Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of exist…
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…