{'fraction_non_alphanumeric': 0.023894862604540025, 'fraction_numerical': 0.007168458781362007, 'mean_word_length': 5.253731343283582, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Continuous Severity Assessment Of Pulmonary Edema On Chest X-ray Using Siamese Convolutional Networks

For physicians to take rapid clinical decisions for patients with congestive heart failure, the assessment of pulmonary edema severity in chest radiographs is vital. While deep learning has been promising in detecting the presence or absence, or even discrete grades of severity, of such edema, prediction of the continuous-valued severity yet remains a challenge. Here, we propose PENet, a deep learning framework to assess the continuous spectrum of pulmonary edema severity from chest X-rays. We present different modes of implementing this network, and demonstrate that our best model outperforms that of earlier work (mean area under the curve of 0.91 over 0.87, for nine comparisons), while saving training data and computation.