ディープラーニング:ライフサイエンスにおける画像解析のためのフレームワーク(Deep learning: a framework for image analysis in life sciences)

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2022-03-11 スイス連邦工科大学ローザンヌ校(EPFL)

生体画像解析において、ディープラーニングモデルが普及しつつある。しかし、標準化が進んでいないことや、専門家でない人がこれらのアルゴリズムを使用することは、偏りの原因となる可能性があります。EPFLと欧州バイオインフォマティクス研究所(EMBL-EBI)の科学者たちは、最近IEEE誌に掲載された論文で、実用的なヒントとガイダンスを提供しています。

Laurène Donati and Virginie Uhlmann © 2022 Alain Herzog


<関連情報>

生体画像解析のための教師ありディープラーニングの実践ガイド: 課題とグッドプラクティス A Practical Guide to Supervised Deep Learning for Bioimage Analysis: Challenges and good practices

Publisher: IEEE  Virginie Uhlmann; Laurène Donati; Daniel Sage

Published in: IEEE Signal Processing Magazine ( Volume: 39, Issue: 2, March 2022)Page(s): 73 – 86
DOI: 10.1109/MSP.2021.3123589

Abstract:

The variety of bioimage data and their quality have dramatically increased over the last decade. In parallel, the number of proposed deep learning (DL) models for their analysis grows by the day. Yet, the adequate reuse of published tools by practitioners without DL expertise still raises many practical questions. In this article, we explore four categories of challenges faced by researchers when using supervised DL models in bioimaging applications. We provide examples in which each challenge arises and review the consequences that inadequate decisions may have. We then outline good practices that can be implemented to address the challenges of each category in a scientifically sound way. We provide pointers to the resources that are already available or in active development to help in this endeavor and advocate for the development of further community-driven standards. While primarily intended as a practical tutorial for life scientists, this article also aims at fostering discussions among method developers around the formulation of guidelines for the adequate deployment of DL, with the ultimate goal of accelerating the adoption of novel DL technologies in the biology community.

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