Research | Year | Objective | Cohort | AUC | Architecture | Framework | Language |
---|---|---|---|---|---|---|---|
Coudray et al. [16] | 2018 | Classification between LUAD, LUSC, and NL; mutation prediction (STK11, EGFR, FAT1, SETBP1, KRAS, and TP53) | TCGA (1634 slides); NYU (340 slides) | 0.970 (classification) 0.733–0.856 (mutation) | Inception-V3 | TensorFlow | Python |
Yu et al. [17] | 2020 | Identification of histological types and gene expression subtypes of NSCLC | ICGC (87 LUAD patients, 38 LUSC patients); TCGA (427 LUAD patients, 457 LUSC patients) | 0.726–0.864 | AlexNet; GoogLeNet; VGGNet-16; ResNet-50 | Caffe | Python |
Gertych et al. [18] | 2019 | Histologic subclassification of LUAD (5 types) | CSMC (50 cases); MIMW (38 cases); TCGA (27 cases) | Accuracy, 0.892 (patch-level) | GoogLeNet; ResNet-50; AlexNet | Caffe | MATLAB |
Wei et al. [19] | 2019 | Histologic subclassification of LUAD (6 types) | DHMC (422 LUAD slides) | 0.986 (patch-level) | ResNet-18 | PyTorch | Python |
Kriegsmann et al. [20] | 2020 | Classification between LUAD, LUSC, SCLC and NL | 80 LUAD, 80 LUSC, 80 SCLC and 30 controls from NCT | 1.000 (after strict QC) | Inception-V3 | Keras (TensorFlow) | R |
Wang et al. [21] | 2020 | Classification between LUAD, LUSC, SCLC, and NL | SUCC (390 LUAD; 361 LUSC; 120 SCLC; and 68 NL slides); TCGA (250 LUAD and 250 LUSC slides in good quality) | 0.856 (for TCGA cohort) | Modified VGG-16 | TensorFlow | Python |
QuPath [22] | 2017 | Tumour identification, biomarker evaluation, batch-processing, and scripting | Specimens of 660 stage II/III colon adenocarcinoma patients from NIB | / | / | / | JAVA |
DeepFocus [23] | 2018 | Detection of out-of-focus regions in WSIs | 24 slides from OSU | / | CNN | TensorFlow | Python |
ConvPath [24] | 2019 | Cell type classification and TME analysis | TCGA (LUAD); NLST; SPORE; CHCAMS | / | CNN | / | MATLAB; R |
HistoQC [25] | 2019 | Digitization of tissue slides | TCGA (450 slides) | / | / | / | HTML5 |
ACD model [26] | 2015 | Colour normalization for H&E-stained WSIs | Camelyon-16 (400 slides); Camelyon-17 (1000 slides); Motic-cervix (47 slides); and Motic-lung (39 slides) | 0.914 (for classification) | ACD | TensorFlow | Python |