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20162022
most citedTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

4k citations · 4k across the 9 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

cs.CV2018

Robust Face Detection via Learning Small Faces on Hard Images

Zhishuai Zhang, Wei Shen, Siyuan Qiao +3

Recent anchor-based deep face detectors have achieved promising performance, but they are still struggling to detect hard faces, such as small, blurred and partially occluded faces…

cs.CV2018

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

Yan Wang, Yuyin Zhou, Wei Shen +3

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is…

cs.CV2018

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

Yan Wang, Yuyin Zhou, Peng Tang +3

Deep convolutional neural networks (CNNs), especially fully convolutional networks, have been widely applied to automatic medical image segmentation problems, e.g., multi-organ seg…

cs.CV2018

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

Yan Wang, Lingxi Xie, Siyuan Qiao +3

Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recogniti…

cs.CV2018

Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

Yuyin Zhou, Yan Wang, Peng Tang +4

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expe…