mlsanityData to 14 Oct 2026

An Aggregation of Aggregation Methods in Computational Pathology

arXiv 2211.01256 ↗First posted 2 Nov 2022cs.CV cs.LGcited by 20

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Abstract (arXiv)

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 38 of its 88 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

38 of 88 references.

References · 38

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26 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 7; 2025: 5; 2026: 3.

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  1. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  2. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 16
  3. 2024HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classificationcited by 13
  4. 2024Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspectivecited by 10
  5. 2023Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric studycited by 6
  6. 2023MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysiscited by 6
  7. 2023Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Imagescited by 5
  8. 2023A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasiacited by 3
  9. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathologycited by 2
  10. 2024WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathologycited by 1
  11. 2024HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modellingcited by 1
  12. 2024Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identificationcited by 0
8 more
  1. 2024Detection of adrenal anomalous findings in spinal CT images using multi model graph aggregationcited by 0
  2. 2025Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classificationcited by 0
  3. 2025Evaluating Multiple Instance Learning Strategies for Automated Sebocyte Droplet Countingcited by 0
  4. 2025Rank-Aware Agglomeration of Foundation Models for Immunohistochemistry Image Cell Countingcited by 0
  5. 2025Interpretable Multimodal Cancer Prototyping with Whole Slide Images and Incompletely Paired Genomicscited by 0
  6. 2025CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learningcited by 0
  7. 2026Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathologycited by 0
  8. 2026Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Gradingcited by 0