no code implementations • 23 Jan 2024 • Nikki Bialy, Frank Alber, Brenda Andrews, Michael Angelo, Brian Beliveau, Lacramioara Bintu, Alistair Boettiger, Ulrike Boehm, Claire M. Brown, Mahmoud Bukar Maina, James J. Chambers, Beth A. Cimini, Kevin Eliceiri, Rachel Errington, Orestis Faklaris, Nathalie Gaudreault, Ronald N. Germain, Wojtek Goscinski, David Grunwald, Michael Halter, Dorit Hanein, John W. Hickey, Judith Lacoste, Alex Laude, Emma Lundberg, Jian Ma, Leonel Malacrida, Josh Moore, Glyn Nelson, Elizabeth Kathleen Neumann, Roland Nitschke, Shuichi Onami, Jaime A. Pimentel, Anne L. Plant, Andrea J. Radtke, Bikash Sabata, Denis Schapiro, Johannes Schöneberg, Jeffrey M. Spraggins, Damir Sudar, Wouter-Michiel Adrien Maria Vierdag, Niels Volkmann, Carolina Wählby, Siyuan, Wang, Ziv Yaniv, Caterina Strambio-De-Castillia
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health.
no code implementations • 7 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.
1 code implementation • 14 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.
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).
no code implementations • 17 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).
1 code implementation • 16 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.
no code implementations • 2 Jul 2019 • Peter Ström, Kimmo Kartasalo, Henrik Olsson, Leslie Solorzano, Brett Delahunt, Daniel M. Berney, David G. Bostwick, Andrew J. Evans, David J. Grignon, Peter A. Humphrey, Kenneth A. Iczkowski, James G. Kench, Glen Kristiansen, Theodorus H. van der Kwast, Katia R. M. Leite, Jesse K. McKenney, Jon Oxley, Chin-Chen Pan, Hemamali Samaratunga, John R. Srigley, Hiroyuki Takahashi, Toyonori Tsuzuki, Murali Varma, Ming Zhou, Johan Lindberg, Cecilia Bergström, Pekka Ruusuvuori, Carolina Wählby, Henrik Grönberg, Mattias Rantalainen, Lars Egevad, Martin Eklund
We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology.
no code implementations • 24 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.
no code implementations • 24 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.