TABLE III.

SUMMARY OF THE PAPERS THAT USED MACHINE LEARNING IN EYE TRACKING IN MAMMOGRAPHIC IMAGE ANALYSIS

Reference	Year	Tracker	# cases	# readers	Gaze data form	ML method	Objective
Ji et al. [45]	2023	Tobii Pro Nano	1364	1	heatmaps	CNN	Contrastive learning on two mammography projections for breast masses analysis
Lou et al. [53]	2023	SMI RED	196	10	heatmaps	VGG-16, U-Net ResNet, MobileNetV2	Comparing different algorithms for the automated generation of gaze heatmaps
Malletal. [86]	2019	ASL H6	120	8	fixations	ResNet, Inception v4, VGG	Using fixations to generate regions of different attention and then applying CNNs to predict the user’s opinion about such regions
Mall et al. [85]	2019	ASL H6	102	8	fixations	SVM, SGD, Gradboost, InseptionsNet, ResNet	Using gaze fixations to extract image patches and predicting radiologist’s decision errors from such patches
Malletal. [82,84]	2018	ASL H6	120	8	fixations	InceptionV2	Using gaze fixations to extract image patches and predict radiological attention level
Yoonetal. [81]	2018	ASL H6	100	10	gaze paths	HMM, CNN, DBN	Converting gaze paths into 2D arrays and classifying target images using these arrays
Gandomkar et al. [77–79]	2017	ASL H6	120	8	fixations	SVM	Classifying false positive and negative tumor detections using gaze as seeds
Voisin et al. [54]	2013	Mirametrix S2	40	6	fixations	SVM, kNN, decision trees	Predicting radiological errors from image patches and gaze features
Tourassi et al. [76]	2013	ASLH6	20	6	fixations	RF, AdaBoost, BayesNet	Predicting radiologists’ attention, decision, and error from gaze and image data
Mello-Thoms et al. [87, 88]	2003	ASL 4000SU	40	9	fixations	PLP	Using gaze fixation as seeds for breast lesion classification

Abbreviations: ML (machine learning), CNN (convolutional neural network), kNN (k-nearest neighbor), SVM (support vector machine), SGD (stochastic gradient descent), HMM (hidden Markov model), DBN (deep belief network), RF (random forest), PLP (parallel level perceptron).