Search Results for author: Fredrik Strand

Found 8 papers, 5 papers with code

PatchDropout: Economizing Vision Transformers Using Patch Dropout

1 code implementation10 Aug 2022 Yue Liu, Christos Matsoukas, Fredrik Strand, Hossein Azizpour, Kevin Smith

This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size.

Image Classification Medical Image Classification

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

2 code implementations2 Dec 2021 Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms.

Benchmarking Ordinal Classification

Adding Seemingly Uninformative Labels Helps in Low Data Regimes

2 code implementations ICML 2020 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

Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.

Tumor Segmentation

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

1 code implementation11 Jul 2020 Yue Liu, Hossein Azizpour, Fredrik Strand, Kevin Smith

With this in mind, we trained networks using three different criteria to select the positive training data (i. e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.

Decision Making

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