Deep Learning on Histopathological Images Enables Accurate Diagnosis of Common B-Cell and Hodgkin Lymphoma

Accurate subtyping of lymphomas is critical for guiding treatment decisions and predicting patient outcomes, yet it remains challenging due to overlapping morphological features. This study aimed to develop and validate a deep learning-based diagnostic system, designated BH-LymphDS, to accurately differentiate the three most prevalent lymphoma subtypes: Diffuse Large B-cell Lymphoma (DLBCL), Follicular Lymphoma (FL), and Hodgkin Lymphoma (HL). The system integrates a Swin Transformer backbone with a clustering-constrained attention multiple instance learning (CLAM) algorithm and a k-nearest neighbors (KNN) classifier, supporting weakly supervised learning directly from whole-slide images without manual region-level annotation. A total of 1910 whole-slide images (WSIs) from diagnostic biopsies were utilized for model development-the largest cohort to date in this field. The performance of nine distinct deep learning architectures was systematically compared. Model efficacy was rigorously evaluated through five-fold cross-validation and further validated on independent external datasets from TCIA and TCGA. Additionally, a comparative analysis was conducted against the diagnostic performance of three experienced hematopathologists. In the internal testing cohort, BH-LymphDS achieved a micro-average AUC of 0.955 and an accuracy of 92.5%. During cross-validation, the system attained a superior micro-average F1-score (0.871) compared to the average of the three hematopathologists (0.851). The Swin Transformer emerged as the optimal backbone, and gradient-weighted class activation mapping (GradCAM) confirmed that the model's decisions were based on legitimate morphological features such as nuclear atypia and cellular growth patterns. Our findings demonstrate that an advanced AI system can satisfy the rigorous requirements for accurate lymphoma subclassification on histopathological slides. The BH-LymphDS shows strong potential as a reliable auxiliary diagnostic tool to enhance clinical workflow efficiency and diagnostic consistency.