{'fraction_non_alphanumeric': 0.02572347266881029, 'fraction_numerical': 0.0010718113612004287, 'mean_word_length': 5.719424460431655, '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}}
Optimizing Feature Representation via A Nested Network for Object Segmentation

Automatic object segmentation based on artificial neural networks is a critical task in an array of real-world applications. Localizing and region segmentation is of particular interest, although typical approaches rely on complex networks and/or human interactions. Therefore, various complex networks suffer from suboptimal segmentation due to inaccurate feature extraction. This paper introduces a Multi-Gated Nested Network (MGN-net) that provides precise segmentation performance by capturing relevant contextual information via a channel gating mechanism. Results utilize challenging biomedical image databases, featuring MRI Brain and Chest X-ray images, are presented. The results show that the MGN-net approach subjectively and objectively performs favorably compared to multiple state-of-the-art methods, such as the U2-net and U-net networks.