Anders Heyden
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- 2019
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Mark
Comparison of different augmentation techniques for improved generalization performance for gleason grading
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
- 2018
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Mark
Generalization of prostate cancer classification for multiple sites using deep learning
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Estimating Uncertainty in Time-difference and Doppler Estimates
2018) 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018 p.245-253(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Relative pose estimation in binocular vision for a planar scene using inter-image homographies
2018) 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018 2018-January. p.568-575(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
- 2017
-
Mark
Automatic Gleason grading of H&E stained microscopic prostate images using deep convolutional neural networks
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Visual Odometry from Two Point Correspondences and Initial Automatic Tilt Calibration
2017) 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) 6. p.340-346(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
- 2016
-
Mark
Recovering Planar Motion from Homographies Obtained using a 2.5-Point Solver for a Polynomial System
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Measuring and Evaluating Bitumen Coverage of Stones using two Different Digital Image Analysis Methods
(
- Contribution to journal › Article
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Mark
Towards Grading Gleason Score using Generically Trained Deep convolutional Neural Networks
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding