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20182021
most citedComputer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?

38 citations · 43 across the 5 of their papers we have counts for

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8 papers · 1 filter

cs.CV2021

LGA-RCNN: Loss-Guided Attention for Object Detection

Xin Yi, Jiahao Wu, Bo Ma +2

Object detection is widely studied in computer vision filed. In recent years, certain representative deep learning based detection methods along with solid benchmarks are proposed,…

cs.CV2021

Self-Paced Uncertainty Estimation for One-shot Person Re-Identification

Yulin Zhang, Bo Ma, Longyao Liu +1

The one-shot Person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from to . The first is model uncertainty, which captures the noise…

cs.CV20211 cited

Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation

Xin Yi, Bo Ma, Yulin Zhang +2

Caused by the difference of data distributions, intra-domain gap and inter-domain gap are widely present in image processing tasks. In the field of image dehazing, certain previous…

cs.CV20201 cited

Automatic classification of multiple catheters in neonatal radiographs with deep learning

Robert D. E. Henderson, Xin Yi, Scott J. Adams +1

We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using…

cs.CV20203 cited

AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection

Longyao Liu, Bo Ma, Yulin Zhang +2

Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce annotated examples, which is challenging and demanding. Ex…

cs.CV2018

Generative Adversarial Network in Medical Imaging: A Review

Xin Yi, Ekta Walia, Paul Babyn

Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probabil…