Fredrik Strand

According to our database, Fredrik Strand authored at least 8 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2023

RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement.

Hong Hui Yeoh, Andrea Liew, Raphael C.-W. Phan, Fredrik Strand, Kartini Rahmat, Tuong Linh Nguyen, John L. Hopper, Maxine Tan

CoRR, 2023

PatchDropout: Economizing Vision Transformers Using Patch Dropout.

Yue Liu, Christos Matsoukas, Fredrik Strand, Hossein Azizpour, Kevin Smith

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers.

Jonathan Karlsson, Fredrik Strand, Josef Bigün, Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Felix Nilsson

Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 2023

2022

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection.

Lidia Garrucho, Kaisar Kushibar, Richard Osuala, Oliver Díaz, Alessandro Catanese, Javier del Riego, Maciej Bobowicz, Fredrik Strand, Laura Igual, Karim Lekadir

CoRR, 2022

2021

CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer.

Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks - the Cohort of Screen-Aged Women (CSAW).

Karin Dembrower, Peter Lindholm, Fredrik Strand

J. Digit. Imaging, 2020

Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models.

Yue Liu, Hossein Azizpour, Fredrik Strand, Kevin Smith

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Adding seemingly uninformative labels helps in low data regimes.

Christos Matsoukas, Albert Bou I Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith

Proceedings of the 37th International Conference on Machine Learning, 2020