Beyond Strong labels: Weakly-supervised Learning Based on Gaussian
Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in
Non-contrast CTs

Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs

5 February 2024

Papers citing "Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs"

18 / 18 papers shown

Title
Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation Peidi Xu O. Sosnovtseva Charlotte Mehlin Sørensen Kenny Erleben S. Darkner 49 4 0 26 May 2023
TotalSegmentator: robust segmentation of 104 anatomical structures in CT images Jakob Wasserthal H. Breit Manfred T. Meyer M. Pradella Daniel Hinck ... Daniel Boll Joshy Cyriac Shan Yang M. Bach Martin Segeroth OOD 65 731 0 11 Aug 2022
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images Ali Hatamizadeh V. Nath Yucheng Tang Dong Yang H. Roth Daguang Xu ViT MedIm 112 1,112 0 04 Jan 2022
The Medical Segmentation Decathlon Michela Antonelli Annika Reinke Spyridon Bakas Keyvan Farahani AnnetteKopp-Schneider ... Zhanwei Xu Yefeng Zheng Amber L. Simpson Lena Maier-Hein M. Jorge Cardoso OOD 106 977 0 10 Jun 2021
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer Wenxuan Wang Chen Chen Meng Ding Jiangyun Li Hong Yu Sen Zha ViT MedIm 77 721 0 07 Mar 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 54 41 0 27 May 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 349 42,299 0 03 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 119 762 0 27 Aug 2019
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation Haidong Zhu Jialin Shi Ji Wu NoLa 59 66 0 27 Jul 2019
Learning to Segment Skin Lesions from Noisy Annotations Z. Mirikharaji Yiqi Yan Ghassan Hamarneh 60 77 0 10 Jun 2019
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations Jiwoon Ahn Sunghyun Cho Suha Kwak ISeg SSeg 142 542 0 10 Apr 2019
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels Shaobo Min Xiao Chen Zhengjun Zha Feng Wu Yongdong Zhang 62 80 0 31 Jul 2018
Attention U-Net: Learning Where to Look for the Pancreas Ozan Oktay Jo Schlemper Loic Le Folgoc M. J. Lee M. Heinrich ... Jingyu Sun Nils Y. Hammerla Bernhard Kainz Ben Glocker Daniel Rueckert SSeg 145 5,018 0 11 Apr 2018
Road Extraction by Deep Residual U-Net Zhengxin Zhang Qingjie Liu Yunhong Wang SSeg 69 2,124 0 29 Nov 2017
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach Yunchao Wei Jiashi Feng Xiaodan Liang Ming-Ming Cheng Yao-Min Zhao Shuicheng Yan 75 808 0 24 Mar 2017
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 598 10,741 0 19 Feb 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 248 19,929 0 07 Oct 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 221 9,298 0 14 Dec 2015

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.