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Data and Pre-Trained Models from "Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation"

appsOther research productkeyboard_double_arrow_right Other ORP type , Other literature type 07 Jul 2021Publisher:Zenodo
Authors: Reuben Dorent; Samuel Joutard; Jonathan Shapey; Aaron Kujawa; Marc Modat; Sebastien Ourselin; Tom Vercauteren;

doi: 10.5281/zenodo.5081986 , 10.5281/zenodo.5081162 , 10.5281/zenodo.5081163

Data and Pre-Trained Models from "Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation"

Abstract

Data and pre-trained models from InExtremIS: Dorent, et al: ""Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation". MICCAI (2021) Description of the data: full_annotations.zip: Manual segmentations performed in consensus by the treating neurosurgeon and physicist. extreme_points.zip: Manual extreme points performed by a biomedical engineer and simulated extreme points generated using the manual segmentations precomputed_geodesics.zip: Pre-computed geodesics using either the image gradient information only or the image gradient information and the Euclidean distance. models.zip: contains the pre-trained models used in the experiments.

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Keywords

Weakly supervised, InExtremIS, Medical Image Segmentation

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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