# Task Fingerprinting for Meta Learning in Biomedical Image Analysis

arXiv: https://arxiv.org/abs/2107.03949 · HTML: https://mlsanity.com/paper/2107.03949/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Task2Vec: Task Embedding for Meta-Learning](https://mlsanity.com/paper/1902.03545/) (2019)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [2017 Robotic Instrument Segmentation Challenge](https://mlsanity.com/paper/1902.06426/) (2019)
- [The Information Complexity of Learning Tasks, their Structure and their Distance](https://mlsanity.com/paper/1904.03292/) (2019)
- [Exploiting the potential of unlabeled endoscopic video data with self-supervised learning](https://mlsanity.com/paper/1711.09726/) (2017)
- [Robust Medical Instrument Segmentation Challenge 2019](https://mlsanity.com/paper/2003.10299/) (2020)
- [Cats or CAT scans: transfer learning from natural or medical image source datasets?](https://mlsanity.com/paper/1810.05444/) (2018)
- [Endoscopy artifact detection (EAD 2019) challenge dataset](https://mlsanity.com/paper/1905.03209/) (2019)
- [Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification](https://mlsanity.com/paper/2007.05009/) (2020)
- [Exploring the similarity of medical imaging classification problems](https://mlsanity.com/paper/1706.03509/) (2017)
- [P2L: Predicting Transfer Learning for Images and Semantic Relations](https://mlsanity.com/paper/1908.07630/) (2019)

## Built on by (most cited first, 6 of 6)

- [MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model](https://mlsanity.com/paper/2410.05905/) (2024)
- [MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis](https://mlsanity.com/paper/2309.15521/) (2023)
- [Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI](https://mlsanity.com/paper/2412.08763/) (2024)
- [AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy](https://mlsanity.com/paper/2310.04461/) (2023)
- [Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study](https://mlsanity.com/paper/2506.06232/) (2025)
- [EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis](https://mlsanity.com/paper/2504.00047/) (2025)

## Abstract (arXiv)

Shortage of annotated data is one of the greatest bottlenecks in biomedical image analysis. Meta learning studies how learning systems can increase in efficiency through experience and could thus evolve as an important concept to overcome data sparsity. However, the core capability of meta learning-based approaches is the identification of similar previous tasks given a new task - a challenge largely unexplored in the biomedical imaging domain. In this paper, we address the problem of quantifying task similarity with a concept that we refer to as task fingerprinting. The concept involves converting a given task, represented by imaging data and corresponding labels, to a fixed-length vector representation. In fingerprint space, different tasks can be directly compared irrespective of their data set sizes, types of labels or specific resolutions. An initial feasibility study in the field of surgical data science (SDS) with 26 classification tasks from various medical and non-medical domains suggests that task fingerprinting could be leveraged for both (1) selecting appropriate data sets for pretraining and (2) selecting appropriate architectures for a new task. Task fingerprinting could thus become an important tool for meta learning in SDS and other fields of biomedical image analysis.