Search Results for author: Kajsa Møllersen

Found 10 papers, 6 papers with code

Fast TILs estimation in lung cancer WSIs based on semi-stochastic patch sampling

no code implementations5 May 2024 Nikita Shvetsov, Anders Sildnes, Lill-Tove Rasmussen Busund, Stig Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas K. Kilvaer

Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges.

Computational Efficiency whole slide images

Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review

no code implementations2 Jun 2023 Masoud Tafavvoghi, Lars Ailo Bongo, Nikita Shvetsov, Lill-Tove Rasmussen Busund, Kajsa Møllersen

In this scoping review, we identified the publicly available datasets of breast H&E stained whole-slide images (WSI) that can be used to develop deep learning algorithms.

Selection bias whole slide images

Accounting for multiplicity in machine learning benchmark performance

1 code implementation10 Mar 2023 Kajsa Møllersen, Einar Holsbø

In this article, we provide a probability distribution for the case of multiple classifiers so that known analyses methods can be engaged and a better SOTA estimate can be provided.

A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

2 code implementations14 Feb 2022 Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher, Lars Ailo Bongo, Thomas K. Kilvaer

Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data.

BIG-bench Machine Learning Cell Detection +1

Instance Segmentation of Microscopic Foraminifera

1 code implementation15 May 2021 Thomas Haugland Johansen, Steffen Aagaard Sørensen, Kajsa Møllersen, Fred Godtliebsen

The model achieves a (COCO-style) average precision of $0. 78 \pm 0. 00$ on the classification and detection task, and $0. 80 \pm 0. 00$ on the segmentation task.

Instance Segmentation Novel Object Detection +4

Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

1 code implementation PLOS ONE 2019 Mike Voets, Kajsa Møllersen, Lars Ailo Bongo

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets.

Diabetic Retinopathy Grading

Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

1 code implementation12 Mar 2018 Mike Voets, Kajsa Møllersen, Lars Ailo Bongo

We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22).

Diabetic Retinopathy Detection Medical Image Segmentation +1

A bag-to-class divergence approach to multiple-instance learning

1 code implementation7 Mar 2018 Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen

A different viewpoint is that the instances are realisations of random vectors with corresponding probability distribution, and that a bag is the distribution, not the realisations.

General Classification Multiple Instance Learning

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