Search Results for author: Carolina Wählby

Found 9 papers, 3 papers with code

Transcriptome-supervised classification of tissue morphology using deep learning

no code implementations7 Dec 2023 Axel Andersson, Gabriele Partel, Leslie Solorzano, Carolina Wählby

Here we conjecture that spatially resolved gene expression, e. i., the transcriptome, can be used as an alternative to manual annotations.

Seeded iterative clustering for histology region identification

1 code implementation14 Nov 2022 Eduard Chelebian, Francesco Ciompi, Carolina Wählby

Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make.

Clustering Segmentation +2

CoMIR: Contrastive Multimodal Image Representation for Registration

1 code implementation NeurIPS 2020 Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Nataša Sladoje

We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).

Image-to-Image Translation

Introducing Hann windows for reducing edge-effects in patch-based image segmentation

no code implementations17 Oct 2019 Nicolas Pielawski, Carolina Wählby

There is a limitation in the size of an image that can be processed using computationally demanding methods such as e. g. Convolutional Neural Networks (CNNs).

Image Segmentation Semantic Segmentation +1

In Silico Prediction of Cell Traction Forces

1 code implementation16 Oct 2019 Nicolas Pielawski, Jianjiang Hu, Staffan Strömblad, Carolina Wählby

Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface.

Whole slide image registration for the study of tumor heterogeneity

no code implementations24 Jan 2019 Leslie Solorzano, Gabriela M. Almeida, Bárbara Mesquita, Diana Martins, Carla Oliveira, Carolina Wählby

Consecutive thin sections of tissue samples make it possible to study local variation in e. g. protein expression and tumor heterogeneity by staining for a new protein in each section.

feature selection Image Registration +1

Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model

no code implementations24 Feb 2018 Gabriele Partel, Giorgia Milli, Carolina Wählby

Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel.

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