> For the complete documentation index, see [llms.txt](https://www.parapathology.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.parapathology.com/appendix/clippings.md).

# Clippings

Full-text captures of articles, papers, and web pages saved with the Obsidian Web Clipper. Each capture keeps its source URL, author, and capture date in frontmatter, and points at the topic notes it informs through `related_to`.

Clippings are living notes like everything else in this vault: annotate, prune, and rewrite them in place until a capture becomes your own synthesis. Verbatim full-text captures carry `publish: false` so they stay out of the public book — drop that flag once a clipping has been rewritten in your own words with a citation.

## In this section

* [Digital and Computational Pathology Applications in Bladder Cancer: Novel Tools Addressing Clinically Pressing Needs](/appendix/clippings/digital-and-computational-pathology-applications-in-bladder-cancer-novel-tools-addressing-clinically.md)
* [Immunohistochemistry in the Differential Diagnosis of... : Applied Immunohistochemistry & Molecular Morphology](/appendix/clippings/immunohistochemistry-in-the-differential-diagnosis-of...-applied-immunohistochemistry-and-molecular.md)
* [Introduction to Cell Profiler: A beginner’s guide to segmentation - YouTube](/appendix/clippings/introduction-to-cell-profiler-a-beginners-guide-to-segmentation-youtube.md)
* [Pancreatic ductal adenocarcinoma and its subtypes: clinical relevance of histopathology and molecular characterization, integrating the key updates of the 2026 WHO classification](/appendix/clippings/pancreatic-ductal-adenocarcinoma-and-its-subtypes-2026-who-classification-virchows-archiv.md)
* [MET expression by immunohistochemistry as a biomarker in pancreatic neuroendocrine tumours](/appendix/clippings/met-expression-by-immunohistochemistry-as-a-biomarker-in-pancreatic-neuroendocrine-tumours.md)
* [Emerging concepts and recent advances in renal cell neoplasia](/appendix/clippings/emerging-concepts-and-recent-advances-in-renal-cell-neoplasia.md)
* [Neuroendocrine Neoplasms of the Urinary Bladder: Integrating Molecular Advances into a Refined Classification System](/appendix/clippings/neuroendocrine-neoplasms-of-the-urinary-bladder-integrating-molecular-advances-into-a-refined-classi.md)
* [Pathology-CoT: learning visual chain-of-thought agents from expert whole-slide image diagnosis behaviour](/appendix/clippings/pathology-cot-learning-visual-chain-of-thought-agents-from-expert-whole-slide-image-diagnosis-behavi.md)
* [Seeds or Parasites Clinical and Histopathological](/appendix/clippings/seeds-or-parasites-clinical-and-histopathological.md)
* [Solving Unpopular Problems: The QuPath Story](/appendix/clippings/solving-unpopular-problems-the-qupath-story-the-pathologist.md)
* [Distance-based evaluation of tumor budding in colorectal cancer](/appendix/clippings/distance-based-evaluation-of-tumor-budding-in-colorectal-cancer.md)
* [Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016](/appendix/clippings/recommendations-for-reporting-tumor-budding-in-colorectal-cancer-based-on-the-international-tumor-bu.md)
* [Towards robust foundation models for digital pathology](/appendix/clippings/towards-robust-foundation-models-for-digital-pathology.md)
* [A distributional robustness margin for pathology foundation models](/appendix/clippings/a-distributional-robustness-margin-for-pathology-foundation-models.md)
* [Awesome Fly: Curated Fruit Fly Connectome Projects](/appendix/clippings/awesome-fly-curated-fruit-fly-connectome-projects.md)
* [Statistical Rethinking (2026 Edition)](/appendix/clippings/statistical-rethinking-2026-edition.md)
* [When Two Wrongs Don't Make a Right: Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology](/appendix/clippings/when-two-wrongs-dont-make-a-right-examining-confirmation-bias-and-the-role-of-time-pressure-during-h.md)
* [Cognitive biases in AI-assisted medical decision making: A structured review as a primer for veterinary and human pathology](/appendix/clippings/cognitive-biases-in-ai-assisted-medical-decision-making-a-structured-review-as-a-primer-for-veterina.md)
* [From Samples to Knowledge 2025: QuPath Training Course](/appendix/clippings/from-samples-to-knowledge-2025-qupath-training-course.md)
* [Navigating foundation model selection in digital pathology through performance evaluation and tradeoff analysis](/appendix/clippings/navigating-foundation-model-selection-in-digital-pathology-through-performance-evaluation-and-tradeo.md)
* [The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth](/appendix/clippings/the-gold-standard-paradox-in-digital-image-analysis-manual-versus-automated-scoring-as-ground-truth.md)
* [Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis](/appendix/clippings/clinical-validation-of-an-ai-based-pathology-tool-for-scoring-of-metabolic-dysfunction-associated-st.md)
* [Ethical guidelines for deploying artificial intelligence applications in the pathology field: Lessons learned from a prospective framework in a large tertiary care academic medical center](/appendix/clippings/ethical-guidelines-for-deploying-artificial-intelligence-applications-in-the-pathology-field-lessons.md)
* [Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology](/appendix/clippings/screening-efficiency-over-experience-rapid-target-detection-in-low-power-field-as-a-modifiable-cogni.md)
* [A Hybrid MIL Approach Leveraging Convolution and State-Space Model for Whole-Slide Image Cancer Subtyping](/appendix/clippings/a-hybrid-mil-approach-leveraging-convolution-and-state-space-model-for-whole-slide-image-cancer-subt.md)
* [Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters](/appendix/clippings/development-and-initial-validation-of-a-deep-learning-algorithm-to-quantify-histological-features-in.md)
* [Multiplex Immunofluorescence Image Analysis with QuPath — Part 1: Understanding Digital Images](/appendix/clippings/multiplex-immunofluorescence-image-analysis-with-qupath-part-1.md)
* [CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification](/appendix/clippings/cytoformer-a-molecularly-supervised-cell-foundation-model-for-histopathology-cell-classification.md)
* [HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides](/appendix/clippings/histoplus-towards-comprehensive-cellular-characterisation-of-h-and-e-slides.md)
* [Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology](/appendix/clippings/artificial-intelligence-enables-quantitative-assessment-of-ulcerative-colitis-histology.md)
* [Considerations for digital pathology displays](/appendix/clippings/considerations-for-digital-pathology-displays.md)
* [A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification](/appendix/clippings/a-feasibility-study-using-quantitative-and-interpretable-histological-analyses-of-celiac-disease-for.md)
* [HistoGen: Histopathology Cell Nuclei Image Generation Tool](/appendix/clippings/histogen-histopathology-cell-nuclei-image-generation-tool.md)
* [Regulatory Science Tools Catalog: Digital Pathology (FDA CDRH)](/appendix/clippings/regulatory-science-tools-catalog-digital-pathology.md)
* [Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples](/appendix/clippings/performance-of-an-artificial-intelligence-model-for-recognition-and-quantitation-of-histologic-featu.md)
* [Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study](/appendix/clippings/towards-deep-learning-based-detection-and-quantification-of-intestinal-metaplasia-on-digitized-gastr.md)
* [Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning](/appendix/clippings/improving-the-accuracy-of-gastrointestinal-neuroendocrine-tumor-grading-with-deep-learning.md)
* [A Deep Learning Model of Histologic Tumor Differentiation as a Prognostic Tool in Hepatocellular Carcinoma](/appendix/clippings/a-deep-learning-model-of-histologic-tumor-differentiation-as-a-prognostic-tool-in-hepatocellular-car.md)
* [A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)](/appendix/clippings/a-deep-learning-based-model-for-assessment-of-autoimmune-hepatitis-from-histology-ai-h.md)
* [Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer](/appendix/clippings/stroma-and-lymphocytes-identified-by-deep-learning-are-independent-predictors-for-survival-in-pancre.md)