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Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine.
To make the network more robust to unanticipated noise perturbations, we use PGD to search for noise patterns that can trigger the network to give over-confident mistakes.
After ten-fold cross-validation, our proposed system achieves a sensitivity of 95. 3% with 0. 5 false positive/scan and a sensitivity of 96. 2% with 1. 0 false positive/scan.
Likewise, combining the findings of radiologist with the detection algorithm only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.
To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions.
Accurate assessment of Lung nodules is a time consuming and error prone ingredient of the radiologist interpretation work.
Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans.
Second, we will demonstrate how to use the weakly labeled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning.
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments.