# Triagem virtual de imagens de imuno-histoqu\\'imica usando redes neurais artificiais e espectro de padr\\~oes

Source: [Triagem virtual de imagens de imuno-histoqu\\'imica usando redes neurais artificiais e espectro de padr\\~oes](<https://www.sota2.com/research/paper/68fa63d91e209131cea01503>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:51:10.308Z

Authors: Higor Neto Lima, Wellington Pinheiro dos Santos, M\\^euser Jorge Silva Valen\\c{c}a

Published: 2017-12-03T18:06:22.000Z

## Abstract

The importance of organizing medical images according to their nature, application and relevance is increasing. Furhermore, a previous selection of medical images can be useful to accelerate the task of analysis by pathologists. Herein this work we propose an image classifier to integrate a CBIR (Content-Based Image Retrieval) selection system. This classifier is based on pattern spectra and neural networks. Feature selection is performed using pattern spectra and principal component analysis, whilst image classification is based on multilayer perceptrons and a composition of self-organizing maps and learning vector quantization. These methods were applied for content selection of immunohistochemical images of placenta and newdeads lungs. Results demonstrated that this approach can reach reasonable classification performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1712.01695>)

[PDF](<https://arxiv.org/pdf/1712.01695.pdf>)