6 citations · 14 across the 12 of their papers we have counts for
5 papers · 1 filter
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…