Related Experiment Video
Updated: Jul 16, 2026
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
Published on: November 19, 2018
u-LINNDA: A protocol for user-optimized lymphoma identification through neural network detection aid
Maximilian Fischer1, Miriam Cindy Maurer2, Robin Peretzke3
1Heidelberg University, Medical Faculty, Grabengasse 1, 69117 Heidelberg, Germany; German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany; Forschungscampus M(2)OLIE, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
Abstract:
Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are aggressive brain tumors that require neurosurgical treatment. For targeted treatment, biopsy and histopathological identification of the tumor entity are necessary. Here, we present a protocol for diagnosing PCNSL using a convolutional neural network (CNN)-based algorithm. We describe steps for installing the u-LINNDA (user-optimized lymphoma identification through neural network detection aid) algorithm, data preparation and preprocessing, and predicting tumor entities using u-LINNDA. We then detail procedures for predicting tumor identity and inspecting the u-LINNDA report.
