digital pathology
PulseAugur coverage of digital pathology — every cluster mentioning digital pathology across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Withdrawn arXiv paper proposed new MIL method for digital pathology
A research paper titled "Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning" was submitted to arXiv, proposing a method to improve Whole Slide Image classification in digital pathology.…
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AI in pathology needs digital infrastructure first, not just algorithms
The future of AI in pathology hinges on robust digital infrastructure rather than solely on advanced algorithms, according to Promise Okeke, CEO of NovoPath. While foundation models and algorithms can analyze slides and…
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New Software Platform Streamlines AI Evaluation in Digital Pathology
Researchers have developed STEP, a new software platform designed to streamline the operational evaluation of AI models in digital pathology. This modular engine facilitates prospective silent trials, allowing AI perfor…
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New hypotheses proposed for causal inference in whole-slice image classification
Researchers have proposed two hypotheses to evaluate causal inference methods in whole-slice image classification, particularly for digital pathology applications like breast cancer diagnosis. The first hypothesis sugge…
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New research tackles medical image segmentation with low-res and few-shot methods
Two new research papers explore advancements in semantic segmentation for medical imaging. The first paper investigates the efficiency of using low-magnification histopathological images with limited annotations for seg…
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New ARReST strategy slashes WSI storage needs for AI retrieval
Researchers have developed a new strategy called ARReST (Antithetical Redundancy Reduction Strategy) to address the storage and retrieval challenges in digital pathology. This method focuses on reducing redundancy by id…
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Digital pathology study finds tile-level AI benchmarks predict slide-level performance
A new study published on arXiv explores the efficiency of using tile-level performance as a proxy for slide-level outcomes in digital pathology. Researchers benchmarked 19 foundation models across 42 slide-level and 16 …
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New LRMIL framework streamlines pathology image analysis
Researchers have developed LRMIL, a novel framework for analyzing whole slide images in digital pathology. This method uses knowledge distillation to transfer information from high-resolution to low-resolution represent…
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New framework offers symbolic explanations for AI in digital pathology
Researchers have developed Symb-xMIL, a new framework for explaining multiple instance learning (MIL) models in digital pathology. Unlike existing heatmap methods, Symb-xMIL quantifies how a model's predictions align wi…