Ida Arvidsson
Ida Arvidsson
Postdoctoral Researcher, Centre for Mathematical Sciences, Lund University
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Cited by
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Generalization of prostate cancer classification for multiple sites using deep learning
I Arvidsson, NC Overgaard, FE Marginean, A Krzyzanowska, A Bjartell, ...
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018
Automatic Gleason grading of H and E stained microscopic prostate images using deep convolutional neural networks
A Gummeson, I Arvidsson, M Ohlsson, NC Overgaard, A Krzyzanowska, ...
Medical Imaging 2017: Digital Pathology 10140, 196-202, 2017
An artificial intelligence–based support tool for automation and standardisation of Gleason grading in prostate biopsies
F Marginean, I Arvidsson, A Simoulis, NC Overgaard, K Åström, A Heyden, ...
European Urology Focus 7 (5), 995-1001, 2021
Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN
J Isaksson, I Arvidsson, K Åaström, A Heyden
2017 International Joint Conference on Neural Networks (IJCNN), 1252-1256, 2017
Comparison of different augmentation techniques for improved generalization performance for gleason grading
I Arvidsson, NC Overgaard, K Åström, A Heyden
2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019 …, 2019
Prediction of obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks
I Arvidsson, NC Overgaard, K Åström, A Heyden, MO Figueroa, JF Rose, ...
2020 25th International Conference on Pattern Recognition (ICPR), 4442-4449, 2021
Classification of point-of-care ultrasound in breast imaging using deep learning
J Karlsson, I Arvidsson, F Sahlin, K Åström, NC Overgaard, K Lång, ...
Medical Imaging 2023: Computer-Aided Diagnosis 12465, 192-200, 2023
Machine learning algorithm for classification of breast ultrasound images
J Karlsson, J Ramkull, I Arvidsson, A Heyden, K Åström, NC Overgaard, ...
Medical Imaging 2022: Computer-Aided Diagnosis 12033, 487-497, 2022
Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera
I Arvidsson, A Davidsson, NC Overgaard, C Pagonis, K Åström, E Good, ...
Journal of Nuclear Cardiology 30 (1), 116-126, 2023
Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks
I Arvidsson, NC Overgaard, A Davidsson, JF Rose, K Aström, ...
Medical Imaging 2021: Computer-Aided Diagnosis 11597, 154-160, 2021
Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases
L Karlsson, J Vogel, I Arvidsson, K Åström, S Janelidze, K Blennow, ...
bioRxiv, 2023.06. 08.544222, 2023
Domain-adversarial neural network for improved generalization performance of Gleason grade classification
I Arvidsson, NC Overgaard, A Krzyzanowska, FE Marginean, A Simoulis, ...
Medical Imaging 2020: Digital Pathology 11320, 284-289, 2020
Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild …
I Arvidsson, O Strandberg, S Palmqvist, E Stomrud, N Cullen, S Janelidze, ...
Alzheimer's Research & Therapy 16 (1), 61, 2024
Adjusting for CSF Reference Proteins Improves Biomarker Accuracy
L Karlsson, I Arvidsson, K Åstr, S Janelidze, S Palmqvist, E Stomrud, ...
Alzheimer's & Dementia 19, e074764, 2023
Systematic Augmentation in HSV Space for Semantic Segmentation of Prostate Biopsies
F Winzell, I Arvidsson, NC Overgaard, K Åström, FE Marginean, A Bjartell, ...
Scandinavian Conference on Image Analysis, 293-308, 2023
Applications of Deep Learning in Medical Image Analysis: Grading of Prostate Cancer and Detection of Coronary Artery Disease
I Arvidsson
Lund University, 2021
Automatic detection of small areas of Gleason grade 5 in prostate tissue using CNN
K Tall, I Arvidsson, NC Overgaard, K Aström, A Heyden
Medical Imaging 2019: Digital Pathology 10956, 57-62, 2019
Heterogeneous Multi-Sensor Camera
I Arvidsson, J Henriksson
LRAP 513, 2016
Towards Super Supervision
M Oskarsson, V Larsson, C Olsson, NC Overgaard, I Arvidsson, G Flood, ...
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