Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
A hitchhiker's guide through the bio‐image analysis software universe
Modern research in the life sciences is unthinkable without computational methods for
extracting, quantifying and visualising information derived from microscopy imaging data of …
extracting, quantifying and visualising information derived from microscopy imaging data of …
Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …
Whole-cell organelle segmentation in volume electron microscopy
Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a
complete understanding of their intricate organization requires the nanometre-level, three …
complete understanding of their intricate organization requires the nanometre-level, three …
Adaptive template transformer for mitochondria segmentation in electron microscopy images
Mitochondria, as tiny structures within the cell, are of significant importance to study cell
functions for biological and clinical analysis. And exploring how to automatically segment …
functions for biological and clinical analysis. And exploring how to automatically segment …
Dualrel: Semi-supervised mitochondria segmentation from a prototype perspective
Automatic mitochondria segmentation enjoys great popularity with the development of deep
learning. However, existing methods rely heavily on the labor-intensive manual gathering by …
learning. However, existing methods rely heavily on the labor-intensive manual gathering by …
Bioimage model zoo: a community-driven resource for accessible deep learning in bioimage analysis
Deep learning-based approaches are revolutionizing imaging-driven scientific research.
However, the accessibility and reproducibility of deep learning-based workflows for imaging …
However, the accessibility and reproducibility of deep learning-based workflows for imaging …
Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset
R Conrad, K Narayan - Cell Systems, 2023 - cell.com
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one
accurately and precisely in any 2D or volume electron microscopy (EM) image is an …
accurately and precisely in any 2D or volume electron microscopy (EM) image is an …
Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited
annotated samples has remained an open challenge for medical imaging data. While pre …
annotated samples has remained an open challenge for medical imaging data. While pre …
Mitochondria in disease: changes in shapes and dynamics
Mitochondrial structure often determines the function of these highly dynamic,
multifunctional, eukaryotic organelles, which are essential for maintaining cellular health …
multifunctional, eukaryotic organelles, which are essential for maintaining cellular health …