Sparse coding of pathology slides compared to transfer learning with deep neural networks

BMC Bioinformatics

Table 1 Matched tumor/non-tumor tissue images

Tissue of origin	Tumor type	Count
Adrenal gland	Pheochromocytoma and Paraganglioma	6
Bile duct	Cholangiocarcinoma	18
Bladder	Bladder Urothelial Carcinoma	45
Breast	Breast Invasive Carcinoma	429
Colon	Colon Adenocarcinoma	130
Colon	Rectum Adenocarcinoma	27
Cervix	Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma	6
Stomach	Stomach Adenocarcinoma	68
Head and neck	Head and Neck Squamous Cell Carcinoma	116
Lung	Lung Adenocarcinoma	179
Lung	Lung Squamous Cell Carcinoma	115
Liver	Liver Hepatocellular Carcinoma	118
Esophagus	Esophageal Carcinoma	16
Pancreas	Pancreatic Adenocarcinoma	8
Prostate	Prostate Adenocarcinoma	124
Kidney	Kidney Chromophobe	69
Kidney	Kidney Renal Clear Cell Carcinoma	214
Kidney	Kidney Renal Papillary Cell Carcinoma	78
Sarcoma	Sarcoma	4
Melanoma (skin)	Skin Cutaneous Melanoma	2
Thyroid	Thyroid Carcinoma	114
Thymus	Thymoma	4
Uterus	Uterine Corpus Endometrial Carcinoma	54

For each tumor from a given patient, at least one slide image was labeled as cancerous (“primary tumor”) and at least one image as “normal” (adjacent samples or clean margin)

ISSN: 1471-2105