Convolutional LSTMを用いた乳房画像の視線動向の予測
書誌事項
- タイトル別名
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- Prediction of Eye Movement on Mammography with Convolutional LSTM
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説明
<p>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<0.05). In the future, it will be necessary to increase the number of cases and further improve the prediction.</p>
収録刊行物
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- 医用画像情報学会雑誌
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医用画像情報学会雑誌 39 (1), 7-13, 2022
医用画像情報学会
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詳細情報 詳細情報について
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- CRID
- 1390573242745613056
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- ISSN
- 18804977
- 09101543
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
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- 抄録ライセンスフラグ
- 使用不可