activity
20172022
most citedImproving Deep Lesion Detection Using 3D Contextual and Spatial Attention

5 citations · 13 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2022

ReCasNet: Improving consistency within the two-stage mitosis detection framework

Chawan Piansaddhayanon, Sakun Santisukwongchote, Shanop Shuangshoti +3

Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is ver…

eess.IV20213 cited

Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization

Wei Feng, Lie Ju, Lin Wang +5

Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based…

cs.CV2020

Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection

Zhonghua Wu, Qingyi Tao, Guosheng Lin +1

Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To…

cs.CV20195 cited

Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention

Qingyi Tao, Zongyuan Ge, Jianfei Cai +2

Lesion detection from computed tomography (CT) scans is challenging compared to natural object detection because of two major reasons: small lesion size and small inter-class varia…

cs.CV2018

M2E-Try On Net: Fashion from Model to Everyone

Zhonghua Wu, Guosheng Lin, Qingyi Tao +1

Most existing virtual try-on applications require clean clothes images. Instead, we present a novel virtual Try-On network, M2E-Try On Net, which transfers the clothes from a model…

cs.CV2018

VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions

Qing Li, Qingyi Tao, Shafiq Joty +2

Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations. We argue that the explanat…