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This study focuses on investigating radiologists' decision-making processes in breast cancer screening, with the aim of exploring the potential of a decision prediction model trained using individual radiologists' decisions. We built decision prediction models based on the radiologists' eye position recordings and the locations where they indicated that a malignant mass was present. We considered 120 mammogram cases read by eight radiologists with different expertise levels. The decisions made were classified into three categories: True Positives (TP), False Negatives (FN), and False Positives (FP), based on the radiologists' marks and the ground truth. Notably, the data for each radiologist was used to train independent radiologist specific models. The marked areas (TPs, FPs) and the False Negative areas were cropped and fed into both base models VGG19 and ResNet50, which were pretrained with the ImageNet dataset. We enhanced both base models by incorporating a Gabor filter layer. The Gabor filter layer, implemented as a 2D convolutional layer with fixed weights, utilizes Gabor filters to extract essential Gabor features from the input. As a result, our approach yields four models tailored for decision prediction for each radiologist – VGG19 and ResNet50 each with and without Gabor filters. The models were analyzed and compared to assess their performance and potential benefits. The results underscored the significance of the radiologist's expertise and consistency in making decisions in determining the model's accuracy. When radiologists' responses are inconsistent regarding similar features across different cases, predicting the decisions using the individual models becomes challenging. Consequently, the models’ performance displayed variation based on individual radiologists’ data.
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