Table 3. Acronyms.

AF	Autofluorescence	LED	Light-emitting diode
ALL	Acute lymphoblastic leukemia	LR	Logistic regression
AOTF	Acousto-optic tunable filters	MED	Minimum euclidean distance
BG	Benign goiter	ML	Maximum likelihood
cGAN	Conditional generative adversarial network	MLP	Multilayer perceptron
CNN	Convolutional neural network	mRMR	Minimum redundancy maximum relevance
CNS	Central nervous system	MSI	Multispectral imaging
DCIS	Ductal carcinoma in situ	MT	Masson’s trichrome stain
DT	Decision trees	NADH	Nicotinamide aenine dinucleotide
EM	Electromagnetic	NCBI	National Center for Biotechnology Information
EVG	Verhoef’s Van Gieson staining	NIH	National Institutes of Health
FAD	Flavin adenine dinucleotide	NIR	Near-infrared
FCM	Fuzzy c-means	NN	Neural networks
FE	Feature extraction	PCA	Principal component analysis
FLDA	Fisher linear discriminant analysis	PICOS	Participants, interventions, comparisons, outcomes and study design
FM	Fontana Masson silver-staining	PLS	Partial least squares
FNA	Fine needle aspiration	PRISMA	Preferred reporting items for systematic reviews and meta-analyses
FTIR	Fourier-transform infrared spectroscopy	PTC	Papillary thyroid carcinoma
GA	Genetic algorithm	RBC	Red blood cell
GFP	Green fluorescent protein	RF	Random forests
GMM	Gaussian mixture model	RGB	Red, Green, and Blue
H&E	Hematoxylin and eosin	RS	Raman spectroscopy
H&N	Head and neck	SAM	Spectral angle mapper
HSI	Hyperspectral imaging	SCC	Squamous cell carcinoma
ICA	Independent component analysis	SFDI	Spatial frequency domain imaging
IF	Immunofluorescence	SID	Spectral information divergence
IHC	Inmunohistochemistry	SVM	Support vector machine
ISH	In-situ hybridization	SWIR	Short-wavelength infrared
kNN	K-nearest neighbors	VNIR	Visible and near-infrared
LBP	Local binary pattern	WBC	White blood cells
LCTF	Liquid crystal tunable filters	WSI	Whole-slide imaging
LDA	Linear discriminant analysis