Artificial intelligence for solid tumour diagnosis in digital pathology

被引：16

作者：

Klein, Christophe ^{[1
]}

Zeng, Qinghe ^{[1
,2
]}

Arbaretaz, Floriane ^{[1
]}

Devevre, Estelle ^{[1
]}

Calderaro, Julien ^{[3
]}

Lomenie, Nicolas ^{[2
]}

Maiuri, Maria Chiara ^{[1
]}

机构：

[1] Univ Paris, Sorbonne Univ, Ctr Histol Imagerie & Cytometrie CHIC, Ctr Rech Cordeliers,INSERM, Paris, France

[2] Univ Paris, Lab Informat Paris Descartes LIPADE, Paris, France

[3] Hop Henri Mondor, Dept Pathol, Creteil, France

来源：

BRITISH JOURNAL OF PHARMACOLOGY | 2021年 / 178卷 / 21期

关键词：

artificial intelligence; cancer; convolutional neural networks; digital pathology; histopathology; WHOLE SLIDE IMAGES; INFILTRATING LYMPHOCYTES; STANDARDIZED METHOD; DIABETIC-RETINOPATHY; CANCER; HISTOPATHOLOGY; CARCINOMA; MELANOMA; CLASSIFICATION; VALIDATION;

D O I：

10.1111/bph.15633

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Tumour diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information due to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms. AI has been successfully used in the field of medical imaging and more recently in digital pathology. The feasibility and usefulness of AI assisted pathology tasks have been demonstrated in the very last years and we can expect those developments to be applied to routine histopathology in the future. In this review, we will describe and illustrate this technique and present the most recent applications in the field of tumour histopathology.

引用

页码：4291 / 4315

页数：25

共 155 条

[1] Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices [J].