{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T20:21:30Z","timestamp":1793478090962,"version":"4.5.0"},"reference-count":75,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT)","award":["KAKEN 23K06454"],"award-info":[{"award-number":["KAKEN 23K06454"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging. Methods: This study designed a convolutional neural network based on ResNet architecture to classify a large series of 221 cases, including 177 follicular lymphoma and 44 reactive lymphoid tissue\/lymphoid hyperplasia, which were stained with hematoxylin and eosin (H&amp;E). Explainable artificial intelligence (XAI) methods were used for interpretability. Results: The series included 1,004,509 follicular lymphoma and 490,506 reactive lymphoid tissue image-patches at 224 \u00d7 244 \u00d7 3, and was partitioned into training (70%), validation (10%), and testing (20%) sets. The performance of the training (training and validation sets) had an accuracy of 99.81%. In the testing set, the performance metrics achieved an accuracy of 99.80% at the image-patch level for follicular lymphoma. The other performance parameters were precision (99.8%), recall (99.8%), false positive rate (0.35%), specificity (99.7%), and F1 score (99.9%). Interpretability was analyzed using three methods: grad-CAM, image LIME, and occlusion sensitivity. Additionally, hybrid partitioning was performed to avoid information leakage using a patient-level independent validation set that confirmed high classification performance. Conclusions: Narrow artificial intelligence (AI) can perform differential diagnosis between follicular lymphoma and reactive lymphoma tissue, but it is task-specific and operates within limited constraints. The trained ResNet convolutional neural network (CNN) may be used as transfer learning for larger series of cases and lymphoma diagnoses in the future.<\/jats:p>","DOI":"10.3390\/cancers17152428","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T15:00:42Z","timestamp":1753196442000},"page":"2428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI)"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6129-8299","authenticated-orcid":false,"given":"Joaquim","family":"Carreras","sequence":"first","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haruka","family":"Ikoma","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yara Yukie","family":"Kikuti","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6130-3879","authenticated-orcid":false,"given":"Shunsuke","family":"Nagase","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Ito","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Makoto","family":"Orita","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sakura","family":"Tomita","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0542-2549","authenticated-orcid":false,"given":"Yuki","family":"Tanigaki","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoya","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohei","family":"Masugi","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/jso.2930140108","article-title":"Lymph node biopsy for diagnosis: A statistical study","volume":"14","author":"Lee","year":"1980","journal-title":"J. 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