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Found 9 papers, 3 papers with code
Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data
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.
Transcriptome-supervised classification of tissue morphology using deep learning
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
Annotations are necessary to develop computer vision algorithms for histopathology, but dense annotations at a high resolution are often time-consuming to make.
CoMIR: Contrastive Multimodal Image Representation for Registration
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).
Introducing Hann windows for reducing edge-effects in patch-based image segmentation
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).
In Silico Prediction of Cell Traction Forces
Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface.
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology.
Whole slide image registration for the study of tumor heterogeneity
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.
Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model
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.
