# A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2103.10178 · HTML: https://mlsanity.com/paper/2103.10178/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning](https://mlsanity.com/paper/1810.12241/) (2018)
- [Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration](https://mlsanity.com/paper/2007.06959/) (2020)
- [Few-Shot Segmentation Propagation with Guided Networks](https://mlsanity.com/paper/1806.07373/) (2018)
- [Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data](https://mlsanity.com/paper/1901.08707/) (2019)
- [Prototype Refinement Network for Few-Shot Segmentation](https://mlsanity.com/paper/2002.03579/) (2020)

## Built on by (most cited first, 17 of 17)

- [Unsupervised Video Object Segmentation via Prototype Memory Network](https://mlsanity.com/paper/2209.03712/) (2022)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning](https://mlsanity.com/paper/2307.09769/) (2023)
- [Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2406.05054/) (2024)
- [RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning](https://mlsanity.com/paper/2409.04298/) (2024)
- [Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning](https://mlsanity.com/paper/2201.06358/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning](https://mlsanity.com/paper/2108.11694/) (2021)
- [Bilateral-ViT for Robust Fovea Localization](https://mlsanity.com/paper/2110.09860/) (2021)
- [Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion](https://mlsanity.com/paper/2408.14427/) (2024)
- [Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights](https://mlsanity.com/paper/2408.13816/) (2024)
- [Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images](https://mlsanity.com/paper/2410.09176/) (2024)
- [Coarse Retinal Lesion Annotations Refinement via Prototypical Learning](https://mlsanity.com/paper/2208.14294/) (2022)
- [DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization](https://mlsanity.com/paper/2302.06961/) (2023)
- [Few-shot Medical Image Segmentation via Cross-Reference Transformer](https://mlsanity.com/paper/2304.09630/) (2023)

## Abstract (arXiv)

Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data. Few-shot segmentation approaches address this issue by learning to transfer knowledge from limited quantities of labeled examples. Incorporating appropriate prior knowledge is critical in designing high-performance few-shot segmentation algorithms. Since strong spatial priors exist in many medical imaging modalities, we propose a prototype-based method -- namely, the location-sensitive local prototype network -- that leverages spatial priors to perform few-shot medical image segmentation. Our approach divides the difficult problem of segmenting the entire image with global prototypes into easily solvable subproblems of local region segmentation with local prototypes. For organ segmentation experiments on the VISCERAL CT image dataset, our method outperforms the state-of-the-art approaches by 10% in the mean Dice coefficient. Extensive ablation studies demonstrate the substantial benefits of incorporating spatial information and confirm the effectiveness of our approach.