Prediction of Eye Movement on Mammography with Convolutional LSTM

It is very difficult for radiologist to correctly detect small calcifications and lesions hidden in dense breast tissue on mammography. There are previous papers that detect lesions of the observer’s eye-tracking information in chest radiography etc. by CNN. Therefore, we investigated in 3ch convLSTM, Autoencoding convLSTM, and U-net convLSTM for deep learning, and aimed to predict the eye-tracking movement in mammography with high accuracy. We obtained gaze-tracking data for four mammography expert radiologists and 15 mammography technologists on 15 abnormal and 15 normal mammographies published by the MIAS. Next, a heat map was created at 4-second intervals, and 3ch convLSTM, Autoencoding convLSTM, and U-net convLSTM was used to predict the heat map image 4 seconds ahead from the temporal two heat map images. In the SSIM in U-net convLSTM, 4-8 seconds to 16-20 seconds was 0.96±0.01. In all 4-8 seconds to 16-20 seconds, the SSIM in U-net convLSTM was higher than this in 3ch convLSTM, Autoencoder convLSTM and there was a statistically significant difference (P