# Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2401.06473 · HTML: https://mlsanity.com/paper/2401.06473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [Masked Image Modeling Advances 3D Medical Image Analysis](https://mlsanity.com/paper/2204.11716/) (2022)
- [SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images](https://mlsanity.com/paper/2012.02383/) (2020)
- [DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis](https://mlsanity.com/paper/2204.10437/) (2022)
- [Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views](https://mlsanity.com/paper/2206.00227/) (2022)
- [Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis](https://mlsanity.com/paper/2302.05615/) (2023)
- [Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images](https://mlsanity.com/paper/2107.08274/) (2021)
- [vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images](https://mlsanity.com/paper/2307.14725/) (2023)

## Built on by (most cited first, 2 of 2)

- [CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration](https://mlsanity.com/paper/2603.23694/) (2026)
- [Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications](https://mlsanity.com/paper/2609.01427/) (2026)

## Abstract (arXiv)

This paper demonstrates a self-supervised framework for learning voxel-wise coarse-to-fine representations tailored for dense downstream tasks. Our approach stems from the observation that existing methods for hierarchical representation learning tend to prioritize global features over local features due to inherent architectural bias. To address this challenge, we devise a training strategy that balances the contributions of features from multiple scales, ensuring that the learned representations capture both coarse and fine-grained details. Our strategy incorporates 3-fold improvements: (1) local data augmentations, (2) a hierarchically balanced architecture, and (3) a hybrid contrastive-restorative loss function. We evaluate our method on CT and MRI data and demonstrate that our new approach particularly beneficial for fine-tuning with limited annotated data and consistently outperforms the baseline counterpart in linear evaluation settings.