Whole slide images for primary diagnostics in dermatopathology: a feasibility study.
PulseAugur coverage of Whole slide images for primary diagnostics in dermatopathology: a feasibility study. — every cluster mentioning Whole slide images for primary diagnostics in dermatopathology: a feasibility study. across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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AI maps demographic data from 1.3M pathology images
Researchers have developed a method to map demographic information from routine clinical histopathology images. By analyzing 1.3 million whole-slide images from over 250,000 patients across 25 organs, they utilized path…
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New GNN predicts gene expression from pathology images across scales
Researchers have developed a novel Multi-Scale Graph Neural Network (GNN) capable of predicting gene expression at various resolutions—bulk, spatial, and single-cell—directly from Whole-Slide Images (WSIs). This model l…
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New framework integrates pathology images and transcriptomics for cancer survival prediction
Researchers have developed a new framework called AM$^2$ES for predicting cancer survival by integrating histopathological Whole-Slide Images (WSIs) and transcriptomic data. This framework addresses limitations in exist…
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New method improves gastric cancer classification using multimodal knowledge distillation
Researchers have developed a new multimodal knowledge distillation framework to improve the classification of gastric adenocarcinoma from whole-slide images. This method efficiently combines image and text data using Lo…
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New pipeline streamlines whole-slide image embedding extraction for pathology
Researchers have developed a new pipeline for extracting embeddings from large whole-slide images (WSIs) used in computational pathology. This system decouples the workflow into patch generation, embedding inference, an…
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PathSelect framework enables efficient WSI processing for vision-language models
Researchers have developed PathSelect, a novel framework for efficiently processing gigapixel Whole-Slide Images (WSIs) with vision-language models. This method reformulates token pruning as a sequential selection proce…
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AI models learn from pathologist attention for efficient histopathology analysis
Researchers have developed two novel approaches for analyzing histopathological images, aiming to improve efficiency and accuracy in medical diagnostics. The first method, SASHA, utilizes deep reinforcement learning and…
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New AI framework improves cancer prognosis analysis using semantic anchors
Researchers have developed a new framework called Semantic-Anchored Evidential Fusion Survival (SAEFS) to improve the accuracy and reliability of whole-slide image analysis for cancer prognosis. SAEFS leverages Visual Q…
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Gastric cancer AI model GRACE boosts pathologist accuracy
Researchers have developed GRACE, a specialized foundation model for gastric cancer pathology, trained on a large dataset of over 48,000 whole-slide images. This model demonstrated superior performance compared to gener…
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AI priors boost colorectal cancer MSI prediction across sites
Researchers have developed a method to improve the generalization of foundation models for predicting microsatellite instability (MSI) status in colorectal cancer from whole slide images. By incorporating biologically m…