# Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections

arXiv: https://arxiv.org/abs/2011.12865 · HTML: https://mlsanity.com/paper/2011.12865/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions](https://mlsanity.com/paper/2207.08975/) (2022)
- [TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography](https://mlsanity.com/paper/2210.07411/) (2022)
- [Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI](https://mlsanity.com/paper/2401.17207/) (2024)
- [Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain](https://mlsanity.com/paper/2311.16821/) (2023)
- [PosDiffAE: Position-aware Diffusion Auto-encoder For High-Resolution Brain Tissue Classification Incorporating Artifact Restoration](https://mlsanity.com/paper/2507.02405/) (2025)
- [CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution](https://mlsanity.com/paper/2511.01870/) (2025)
- [2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks](https://mlsanity.com/paper/2103.05259/) (2021)
- [From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging](https://mlsanity.com/paper/2505.11394/) (2025)
- [CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training](https://mlsanity.com/paper/2601.12282/) (2026)

## Abstract (arXiv)

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.