脑肿瘤评估中的比较诊断使得在评估新患者时可以使用医疗中心的可用信息来比较类似病例。通过利用人工智能模型，所提出的系统能够针对给定的查询检索最相似的脑肿瘤病例。主要目标是通过生成更准确的医学图像表示来增强诊断过程，特别关注患者特定的正常特征和病理。与以前的模型的一个关键区别在于它能够仅从二进制信息生成丰富的图像描述符，从而消除了昂贵且难以获得的肿瘤分割的需要。所提出的模型使用人工智能来检测患者特征，以推荐最相似的病例数据库。该系统不仅建议类似的案例，而且在设计中平衡了健康和异常特征的表示。这不仅鼓励其使用的普遍化，而且还有助于临床医生的决策过程。这种概括使得未来在不同医学诊断领域的研究成为可能，而系统几乎没有任何变化。我们对我们的方法与类似研究进行了比较分析。所提出的架构在患者的肿瘤和健康区域中获得了 0.474 的 Dice 系数，优于之前的文献。我们提出的模型擅长从脑磁共振（MR）中提取和组合解剖学和病理学特征，在依赖较便宜的标签信息的同时实现最先进的结果。这大大降低了培训过程的总体成本。我们的研究结果强调了提高比较诊断和肿瘤病理治疗的效率和准确性的巨大潜力。本文为进一步探索所提出的架构的更广泛适用性和优化以增强临床决策提供了坚实的基础。这项工作中提出的新颖方法标志着医学诊断领域的重大进步，特别是在人工智能辅助图像检索的背景下，并有望使用人工智能作为支持工具来降低成本并提高患者护理质量黑匣子系统。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation.The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system.We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies.This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.