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Found 15 papers, 3 papers with code
Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features
The ability to extrapolate gene expression dynamics in living single cells requires robust cell segmentation, and one of the challenges is the amorphous or irregularly shaped cell boundaries.
Improving Across-Dataset Brain Tissue Segmentation Using Transformer
Transformers have demonstrated success in natural image segmentation and have recently been applied to 3D medical image segmentation tasks due to their ability to capture long-distance relationships in the input where the local receptive fields of CNNs struggle.
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis.
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD).
Discriminative analysis of the human cortex using spherical CNNs - a study on Alzheimer's disease diagnosis
In neuroimaging studies, the human cortex is commonly modeled as a sphere to preserve the topological structure of the cortical surface.
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.
Substituting Gadolinium in Brain MRI Using DeepContrast
Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning
Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy.
Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening
Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images.
Automated Multi-Process CTC Detection using Deep Learning
Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor prognosis.
TABSurfer: a Hybrid Deep Learning Architecture for Subcortical Segmentation
Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans.
Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans.
Unsupervised Airway Tree Clustering with Deep Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation.
Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
Pulmonary emphysema, the progressive, irreversible loss of lung tissue, is conventionally categorized into three subtypes identifiable on pathology and on lung computed tomography (CT) images.
