# Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections Source: [Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:07:52.793Z Authors: Christian Schiffer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid Published: 2020-11-25T16:44:23.000Z ## Abstract Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections. Recent work provided the first automatic segmentations of cytoarchitectonic areas in the visual system using Convolutional Neural Networks. We aim to extend this approach to become applicable to a wider range of brain areas, envisioning a solution for mapping the complete human brain. Inspired by recent success in image classification, we propose a contrastive learning objective for encoding microscopic image patches into robust microstructural features, which are efficient for cytoarchitectonic area classification. We show that a model pre-trained using this learning task outperforms a model trained from scratch, as well as a model pre-trained on a recently proposed auxiliary task. We perform cluster analysis in the feature space to show that the learned representations form anatomically meaningful groups. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()