6 citations · 14 across the 12 of their papers we have counts for
13 papers
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
Yinxiang Yu, Maoxiang Chu, Qi Niu +7
Traditional Chinese Medicine (TCM) ocular inspection provides empirical cues for assessing scleral surface anomalies, but its clinical use remains subjective and difficult to quant…
PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and Consistency
Haotian Wang, Aoran Xiao, Xiaoqin Zhang +2
Generalizable depth completion enables the acquisition of dense metric depth maps for unseen environments, offering robust perception capabilities for various downstream tasks. How…
Scale Propagation Network for Generalizable Depth Completion
Haotian Wang, Meng Yang, Xinhu Zheng +1
Depth completion, inferring dense depth maps from sparse measurements, is crucial for robust 3D perception. Although deep learning based methods have made tremendous progress in th…
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data
Haotian Wang, Meng Yang, Nanning Zheng
Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and locat…
Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network
Bryar Shareef, Min Xian, Aleksandar Vakanski +1
Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated relia…
Enhanced Sharp-GAN For Histopathology Image Synthesis
Sujata Butte, Haotian Wang, Aleksandar Vakanski +1
Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. However, existing methods struggle to pro…
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
Haotian Wang, Min Xian, Aleksandar Vakanski +1
Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered…
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…