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Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

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Related Experiment Video

Updated: Jun 29, 2025

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

Published on: December 6, 2016

Deceptive learning in histopathology.

Sahar Shahamatdar^1,2, Daryoush Saeed-Vafa³, Drew Linsley^4,5

¹Center for Computational Molecular Biology, Brown University, Providence, RI, USA.

|April 1, 2024

Summary

This summary is machine-generated.

Deep neural networks (DNNs) show promise in histopathology but can learn deceptive strategies. While trustworthy for tumor detection, DNNs failed to generalize for molecular profiling due to spurious correlations.

Keywords:

KRAS computational pathology deep learning explainable artificial intelligence molecular profiling

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Last Updated: Jun 29, 2025

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Area of Science:

Computational pathology
Artificial intelligence in medicine
Histopathology image analysis

Background:

Deep learning (DL) offers potential for automating histopathology tasks and discovering novel biological insights.
Systematic evaluation of the trustworthiness of visual strategies learned by DL models in histopathology is lacking.

Purpose of the Study:

To systematically evaluate deep neural networks (DNNs) trained for histopathological analysis.
To determine if DNNs' learned strategies are trustworthy or deceptive.

Main Methods:

Trained various DNNs on 221 whole-slide images (WSIs) of lung adenocarcinoma.
Evaluated DNNs on molecular profiling (KRAS vs. EGFR mutations), primary tissue determination, and tumor detection.

Main Results:

DNNs achieved above-chance performance in molecular profiling by exploiting correlations between histological subtypes and mutations, failing to generalize to laser capture microdissection (LCM) test sets.
DNNs learned robust and trustworthy strategies for primary tissue determination and tumor detection/localization.

Conclusions:

DNNs show promise for aiding pathologists but can learn deceptive strategies using spurious correlations, rendering them unsuitable for research or clinical use.
A proposed framework for model evaluation and interpretation is crucial for developing reliable automated histopathological analysis systems.