The present work proposes a Multi-Output Classification Autoencoder (MOC-AE) algorithm to extract features from brain tumour images. The proposed algorithm is able to focus on both the normal features of the patient and the pathological features present in the case, resulting in a compact and significant representation of each image. The architecture of MOC-AE combines anatomical information from the patients scan using an Autoencoder (AE) with information related to a specific pathology using a classification output with the same image descriptor. This combination of goals forces the network to maintain a balance between anatomical and pathological features of the case while maintaining the low cost of the labels being used. The results obtained are compared with those of similar studies and the strengths and limitations of each approach are discussed. The results demonstrate that the proposed algorithm is capable of achieving state-of-the-art results in terms of both the anatomical and tumor characteristics of the recommended cases.
翻译:目前的工作提议采用多输出分类自动编码器(MOC-AE)算法,从脑肿瘤图像中提取特征,拟议的算法能够侧重于病人的正常特征和病例中存在的病理特征,从而对每个图像进行紧凑和重要的描述。MOC-AE的结构将使用Autoencoder(AE)进行的病人扫描提供的解剖学信息与使用同一图像描述器的分类输出与特定病理学相关的信息结合起来。这种目标的结合迫使网络保持病例的解剖和病理特征之间的平衡,同时保持所使用标签的低成本。所取得的结果与类似研究的结果进行比较,并讨论了每种方法的优点和局限性。结果表明,拟议的算法能够从建议案例的解剖和肿瘤特征两方面取得最新的结果。