本研究提出了一种替代的分析工作流，该工作流结合了中红外（MIR）显微成像和深度学习，以诊断人类淋巴瘤并区分小细胞和大细胞淋巴瘤。我们展示了利用深度学习方法来分析从良性和恶性淋巴结病理获得的MIR高光谱数据，可以实现高准确度的正确分类，学习了3900 cm-1到850 cm-1的独特区域。对于每对恶性淋巴组织，准确度超过95%，对于良性和恶性淋巴组织的二分类区分仍然高于90%。这些结果表明，通过应用深度学习方法来分析MIR光谱数据，可以简化人类淋巴瘤的初步诊断和亚型分类。本文受版权保护。保留所有权利。

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 cm-1 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.