[1]XIE J,LIU R,LUTTRELL J,et al.Deep learning based analysis of histopathological images of breast cancer[J].Frontiers in Genetics,2019,10:80-99.
[2]KHAMENEH FD,RAZAVI S,KAMASAK M,et al.Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network[J].Computers in Biology and Medicine,2019,110:164-174.
[3]AZAM AS,MILIGY IM,KIMANI PK,et al.Diagnostic concordance and discordance in digital pathology:a systematic review and meta-analysis[J].J Clin Pathol,2021,74(7):448-455.
[4]RETAMERO JA,ANEIROS-FERNANDEZ J,DEL MORAL RG,et al.Com-plete digital pathology for routine histopathology diagnosis in a multicenter hospital network[J].Arch Pathol Lab Med,2020,144(2):221-228.
[5]李斌,李科宇,汤渝玲,等.基于深度学习的肺癌计算机辅助诊断[J].当代医学,2021,27(9):89-93.
LI B, LI KY, TANG YL,et al.Computer-aided diagnosis of lung carcinoma using deep learning[J].Contemporary Medicine,2021,27(9):89-93.
[6]VALIERIS R,AMARO L,CABDT OSORIO,et al.Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer[J].Cancers,2020,12(12):3687-3698.
[7]KOS Z,DABBS DJ.Biomarker assessment and molecular testing for prognostication in breast cancer[J].Histopathology,2016,68(1):70-85.
[8]HOSSAIN MS,HANNA MG,URAOKA N,et al.Automatic quantification of HER2 gene amplification in invasive breast cancer from chromogenic in situ hybridization whole slide images[J].Journal of Medical Imaging,2019,6(4):047501-1-14.
[9]王秀红,陈皇,宋志刚,等.基于乳腺病理组织学的 HER2智能预测[J].中华病理学杂志,2021,50(4):344-348.
WANG XH, CHEN H, SONG ZG, et al.Intelligent prediction of HER2 status based on breast histopathology[J].Chin J Pathol,2021,50(4):344-348.
[10]SHAMAI G,BINENBAUM Y,SLOSSBERG R,et al.Artificial intelligence algorithms to assess hormonal status from tissue microarrays in patients with breast cancer[J].JAMA Network Open,2019,2(7):e197700.
[11]NAIK N,MADANI A,ESTEVA A,et al.Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains[J].Nat Commun,2020,11(1):1-8.
[12]BERA K,KATZ I,MADABHUSHI A,et al.Re-imagining T staging through artificial intelligence and machine learning image processing approaches in digital pathology[J].JCO Clin Cancer Inform,2020,4:1039-1050.
[13]JABER MI,SONG B,TAYLOR C,et al.A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival[J].Breast Cancer Research,2020,22(1):12-21.
[14]MERECAN E,MEHTA S,BARTLETT J,et al.Assessment of machine learning of breast pathology structures for automated differentiation of breast cancer and high-risk proliferative lesions[J].JAMA Network Open,2019,2(8):7-17.
[15]HAMEED Z,ZAHIA S,GARCIA-ZAPIRAIN B,et al.Breast cancer histopathology image classification using an ensemble of deep learning models[J].Sensors(Basel),2020,20(16):4373-4390.
[16]ABDOLAHI M,SALEHI M,SHOKATIAN I,et al.Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images[J].Med J Islam Repub Iran,2020,34:140-148.
[17]COUTURE HD, WILLIAMS LA,JOSEPH G,et al.Image analysis with deep learning to predict breast cancer grade,ER status,histologic subtype,and intrinsic subtype[J].NPJ Breast Cancer,2018,4(30):1-8.
[18]EHTESHAMI BB,MULLOOLY M,PFEIFFERf RM,et al.Using deep convolutional neural networks to identify and classify tumor associated stroma in diagnostic breast biopsies[J].Mod Pathol,2018,31(10):1502-1512.
[19]MAHMOOD T,ARSALAN M,OWAIS M,et al.Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs[J].Journal of Clinical Medicine,2020,9(3):749-773.
[20]ZHENG X,YAO Z,HUANG Y,et al.Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J].Nat Commun,2020,11(1):1-9.
[21]STEINER DF,MACDONALD R,LIU Y,et al.Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer[J].Am J Surg Pathol,2018,42(12):1636-1646.
[22]DIHGE L,OHLSSON M,EDEN P,et al.Artificial neural network models to predict nodal status in clinically node-negative breast cancer[J].BMC Cancer,2019,19(1):610-621.
[23]FU MR,WANG Y,LI C,et al.Machine learning for detection of lymphedema among breast cancer survivors[J].Mhealth,2018,4:17-27.
[24]BEJNORDI BE,VET M,JOHANNES VAN DIEST P,et al.Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J].JAMA,2017,318(22):2199-2210.
[25]LIU L,KOHLBEERGER T,NOROUZI M,et al.Artificial intelligence-based breast cancer nodal metastasis detection:Insights into the black box for pathologists[J].Arch Pathol Lab Med,2019,143(7):859-868.
[26]KLIMOV S,MILIGY IM,GERTYCH A,et al.A whole slide image-based machine learning approach to predict ductal carcinoma in situ(DCIS) recurrence risk[J].Breast Cancer Research,2019,21:83-102.
[27]CHEN H,GAO M,ZHANG Y,et al.Attention-based multi-NMF deep neural network with multimodality data for breast cancer prognosis model[J].BioMed Research International,2019,1:1-11.
[28]TURKKI R,BYCKHOV D,LUNDIN M,et al.Breast cancer outcome prediction with tumour tissue images and machine learning[J].Breast Cancer Research and Treatment,2019,177:41-52.