# Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification

arXiv: https://arxiv.org/abs/2009.13698 · HTML: https://mlsanity.com/paper/2009.13698/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [A Petri Dish for Histopathology Image Analysis](https://mlsanity.com/paper/2101.12355/) (2021)
- [Curriculum Direct Preference Optimization for Diffusion and Consistency Models](https://mlsanity.com/paper/2405.13637/) (2024)
- [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://mlsanity.com/paper/2212.00532/) (2022)
- [Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology](https://mlsanity.com/paper/2108.07183/) (2021)
- [Learning Rate Curriculum](https://mlsanity.com/paper/2205.09180/) (2022)
- [Learning to Learn: How to Continuously Teach Humans and Machines](https://mlsanity.com/paper/2211.15470/) (2022)
- [Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation](https://mlsanity.com/paper/2302.01049/) (2023)
- [SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis](https://mlsanity.com/paper/2209.02412/) (2022)
- [Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks](https://mlsanity.com/paper/2407.07853/) (2024)
- [On the Efficiency of Subclass Knowledge Distillation in Classification Tasks](https://mlsanity.com/paper/2109.05587/) (2021)
- [Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images](https://mlsanity.com/paper/2110.10381/) (2021)
- [Calibrating Histopathology Image Classifiers using Label Smoothing](https://mlsanity.com/paper/2201.11866/) (2022)
- [Subclass Knowledge Distillation with Known Subclass Labels](https://mlsanity.com/paper/2207.08063/) (2022)
- [CBM: Curriculum by Masking](https://mlsanity.com/paper/2407.05193/) (2024)
- [GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation](https://mlsanity.com/paper/2501.02598/) (2025)
- [Task Phasing: Automated Curriculum Learning from Demonstrations](https://mlsanity.com/paper/2210.10999/) (2022)
- [Confidence-Aware Calibration and Scoring Functions for Curriculum Learning](https://mlsanity.com/paper/2301.12589/) (2023)
- [CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification](https://mlsanity.com/paper/2505.01741/) (2025)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding](https://mlsanity.com/paper/2212.11511/) (2022)
- [On the Perception of Difficulty: Differences between Humans and AI](https://mlsanity.com/paper/2304.09803/) (2023)
- [False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy](https://mlsanity.com/paper/2403.02639/) (2024)
- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Curriculum Based Multi-Task Learning for Parkinson's Disease Detection](https://mlsanity.com/paper/2302.13631/) (2023)
- [ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals](https://mlsanity.com/paper/2308.03936/) (2023)
- [Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy](https://mlsanity.com/paper/2411.01479/) (2024)
- [Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning](https://mlsanity.com/paper/2510.08393/) (2025)
- [Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation](https://mlsanity.com/paper/2601.08127/) (2026)

## Abstract (arXiv)

Applying curriculum learning requires both a range of difficulty in data and a method for determining the difficulty of examples. In many tasks, however, satisfying these requirements can be a formidable challenge. In this paper, we contend that histopathology image classification is a compelling use case for curriculum learning. Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example. Hence, we propose a simple curriculum learning method that trains on progressively-harder images as determined by annotator agreement. We evaluate our hypothesis on the challenging and clinically-important task of colorectal polyp classification. Whereas vanilla training achieves an AUC of 83.7% for this task, a model trained with our proposed curriculum learning approach achieves an AUC of 88.2%, an improvement of 4.5%. Our work aims to inspire researchers to think more creatively and rigorously when choosing contexts for applying curriculum learning.