AI Medical Compendium Topic

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Staining and Labeling

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High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning.

Journal of visualized experiments : JoVE
Surgical margin analysis (SMA), an essential procedure to confirm the complete excision of cancerous tissue in tumor resection surgery, requires intraoperative diagnostic tools to avoid repeated surgeries due to a positive surgical margin. Recently, ...

Swarm learning for decentralized artificial intelligence in cancer histopathology.

Nature medicine
Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obsta...

MVFStain: Multiple virtual functional stain histopathology images generation based on specific domain mapping.

Medical image analysis
To the best of our knowledge, artificial intelligence stain generation is an urgent requirement for histopathology images. Pathological examinations usually only utilize hematoxylin and eosin (H&E) regular staining to show histomorphological characte...

Rethinking ImageNet Pre-training for Computational Histopathology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on ...

A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of ...

Transformer-based unsupervised contrastive learning for histopathological image classification.

Medical image analysis
A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large annotations is very challenging, especially for histopathological images with unique characteristics (...

CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images.

Medical image analysis
Visual representation extraction is a fundamental problem in the field of computational histopathology. Considering the powerful representation capacity of deep learning and the scarcity of annotations, self-supervised learning has emerged as a promi...

Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures...

Deep learning-assisted co-registration of full-spectral autofluorescence lifetime microscopic images with H&E-stained histology images.

Communications biology
Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely c...

Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.

Nature communications
Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxyl...