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20172024
most citedSemi-Supervised Medical Image Segmentation via Learning Consistency under Transformations

187 citations · 217 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CV2022

ATCON: Attention Consistency for Vision Models

Ali Mirzazadeh, Florian Dubost, Maxwell Pike +4

Attention--or attribution--maps methods are methods designed to highlight regions of the model's input that were discriminative for its predictions. However, different attention ma…

cs.CV2019187 cited

Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations

Gerda Bortsova, Florian Dubost, Laurens Hogeweg +2

The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervis…

cs.CV2019

Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks

Florian Dubost, Hieab Adams, Pinar Yilmaz +6

Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated…

cs.CV2018

Deep Learning from Label Proportions for Emphysema Quantification

Gerda Bortsova, Florian Dubost, Silas Ørting +5

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts…

cs.CV2018

Hydranet: Data Augmentation for Regression Neural Networks

Florian Dubost, Gerda Bortsova, Hieab Adams +4

Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annot…

cs.CV2018

Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks

Filipe Marques, Florian Dubost, Mariette Kemner-van de Corput +2

Cystic fibrosis is a genetic disease which may appear in early life with structural abnormalities in lung tissues. We propose to detect these abnormalities using a texture classifi…