activity
20182023
most citedPathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

26 citations · 36 across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV2019

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

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). Some imaging modaliti…

eess.IV2019

In Silico Prediction of Cell Traction Forces

Nicolas Pielawski, Jianjiang Hu, Staffan Strömblad +1

Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface. Typically, TFM requires culturing cells on g…

cs.CV201926 cited

Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Peter Ström, Kimmo Kartasalo, Henrik Olsson +29

Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-…

cs.CV201910 cited

Whole slide image registration for the study of tumor heterogeneity

Leslie Solorzano, Gabriela M. Almeida, Bárbara Mesquita +3

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 secti…

q-bio.QM2018

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

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. This is…