Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image
Segmentation

Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

27 July 2019

Ji Wu

Papers citing "Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation"

7 / 7 papers shown

Title
Learning to Segment from Noisy Annotations: A Spatial Correction Approach Jiacheng Yao Yikai Zhang Songzhu Zheng Mayank Goswami Prateek Prasanna Chao Chen 33 15 0 21 Jul 2023
Weakly Supervised Few-Shot Segmentation Via Meta-Learning P. H. T. Gama Hugo Oliveira J. M. Junior J. D. Santos VLM 19 19 0 03 Sep 2021
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation Shuailin Li Zhitong Gao Xuming He NoLa 25 26 0 21 Jul 2021
Distilling effective supervision for robust medical image segmentation with noisy labels Jialin Shi Ji Wu NoLa 11 32 0 21 Jun 2021
Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation Jingyang Zhang Guotai Wang Hongzhi Xie Shuyang Zhang Ning Huang Shaoting Zhang Lixu Gu 22 41 0 27 May 2020
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis Davood Karimi Haoran Dou Simon K. Warfield Ali Gholipour NoLa 11 534 0 05 Dec 2019
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation Nima Tajbakhsh Laura Jeyaseelan Q. Li J. Chiang Zhihao Wu Xiaowei Ding 13 750 0 27 Aug 2019