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Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights

Ardhendu Sekhar, Ravi Kant Gupta, Amit Sethi

TL;DR

Histopathology faces scarce labeled data, motivating the assessment of few-shot learning. The paper evaluates five state-of-the-art methods—Prototypical Networks, MAML, SimpleShot, LaplacianShot, and DeepEMD—on four histology datasets under $5$-way $N$-shot tasks, revealing that standard fine-tuning-based approaches can outperform episodic meta-learning in this domain. In particular, $5$-way $10$-shot accuracies reach about $85\%$–$90\%$, with some results nearing full-training performance, highlighting the practical viability of few-shot learning when labeled data are limited. The findings also underscore the impact of dataset choice and domain shifts, and point to future directions such as multi-modal integration, transfer learning, explainability, and active learning to further advance medical image classification under data scarcity.

Abstract

This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity of labeled data in medical imaging and the inherent challenges posed by diverse tissue types and data preparation techniques, this research evaluates the performance of state-of-the-art few-shot learning methods for various scenarios on histology data. We have considered four histopathology datasets for few-shot histopathology image classification and have evaluated 5-way 1-shot, 5-way 5-shot and 5-way 10-shot scenarios with a set of state-of-the-art classification techniques. The best methods have surpassed an accuracy of 70%, 80% and 85% in the cases of 5-way 1-shot, 5-way 5-shot and 5-way 10-shot cases, respectively. We found that for histology images popular meta-learning approaches is at par with standard fine-tuning and regularization methods. Our experiments underscore the challenges of working with images from different domains and underscore the significance of unbiased and focused evaluations in advancing computer vision techniques for specialized domains, such as histology images.

Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights

TL;DR

Histopathology faces scarce labeled data, motivating the assessment of few-shot learning. The paper evaluates five state-of-the-art methods—Prototypical Networks, MAML, SimpleShot, LaplacianShot, and DeepEMD—on four histology datasets under -way -shot tasks, revealing that standard fine-tuning-based approaches can outperform episodic meta-learning in this domain. In particular, -way -shot accuracies reach about –, with some results nearing full-training performance, highlighting the practical viability of few-shot learning when labeled data are limited. The findings also underscore the impact of dataset choice and domain shifts, and point to future directions such as multi-modal integration, transfer learning, explainability, and active learning to further advance medical image classification under data scarcity.

Abstract

This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity of labeled data in medical imaging and the inherent challenges posed by diverse tissue types and data preparation techniques, this research evaluates the performance of state-of-the-art few-shot learning methods for various scenarios on histology data. We have considered four histopathology datasets for few-shot histopathology image classification and have evaluated 5-way 1-shot, 5-way 5-shot and 5-way 10-shot scenarios with a set of state-of-the-art classification techniques. The best methods have surpassed an accuracy of 70%, 80% and 85% in the cases of 5-way 1-shot, 5-way 5-shot and 5-way 10-shot cases, respectively. We found that for histology images popular meta-learning approaches is at par with standard fine-tuning and regularization methods. Our experiments underscore the challenges of working with images from different domains and underscore the significance of unbiased and focused evaluations in advancing computer vision techniques for specialized domains, such as histology images.
Paper Structure (12 sections, 31 equations, 6 figures, 1 table)

This paper contains 12 sections, 31 equations, 6 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODOLOGY
  4. Prototypical Networks
  5. Model Agnostic Meta Learning(MAML)
  6. SimpleShot
  7. LalplacianShot
  8. DeepEMD
  9. EXPERIMENTS AND RESULTS
  10. Dataset
  11. Results and Discussion
  12. CONCLUSION

Figures (6)

  • Figure 1: The diagram illustrates a few-shot learning model, showcasing its ability to effectively generalize and recognize classes for unlabeled query set with only a limited number of support examples.The 5 different colors in support set represent 5 different classes(ways) having 1 sample(shot) each.
  • Figure 2: Snapshot of sample images of each class from NCT (top 2 rows) and CRC-TP (bottom row) of FHIST dataset.
  • Figure 3: An example of 5-way 1-shot 2-query episode. The first row represents the support set from 5 different classes of LC25000 dataset. The last two rows represent the query set.
  • Figure 4: Accuracy plot of MAML on different datasets
  • Figure 5: Accuracy plot of DeepEMD on different datasets
  • ...and 1 more figures