{'fraction_non_alphanumeric': 0.019262981574539362, 'fraction_numerical': 0.0, 'mean_word_length': 5.638888888888889, '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}}
Multi-scale Features for Weakly Supervised Lesion Detection of Cerebral Hemorrhage with Collaborative Learning

Deep networks have recently been applied to medical assistant diagnosis. The brain is the largest and the most complex structure in the central nervous system, which is also complicated in medical images such as computed tomography (CT) scan. While reading the CT image, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report. To automate this process, we quantitatively analyze the cerebral hemorrhage dataset and propose a Multi-scale Feature with Collaborative Learning (MFCL) strategy in terms of Weakly Supervised Lesion Detection (WSLD), which not only adapts to the characteristics of detecting small lesions but also introduces the global constraint classification objective in training. Specifically, a multi-scale feature branch network and a collaborative learning are designed to locate the lesion area. Experimental results demonstrate that the proposed method is valid on the cerebral hemorrhage dataset, and a new baseline of WSLD is established on cerebral hemorrhage dataset.