mlsanityData to 14 Oct 2026

Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification

arXiv 2503.10510 ↗First posted 13 Mar 2025q-bio.QM cs.LG quant-phcited by 0

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Abstract (arXiv)

Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this problem. However, the effect of attention mechanism architecture on model performance is not well-documented for biomedical imagery. In this work, we compare different methods and implementations of MIL, including deep learning variants. We introduce a new method using higher-dimensional feature spaces for deep MIL. We also develop a novel algorithm for whole-slide image classification where extreme machine learning is combined with attention-based MIL to improve sensitivity and reduce training complexity. We apply our algorithms to the problem of detecting circulating rare cells (CRCs), such as erythroblasts, in peripheral blood. Our results indicate that nonlinearities play a key role in the classification, as removing them leads to a sharp decrease in stability in addition to a decrease in average area under the curve (AUC) of over 4%. We also demonstrate a considerable increase in robustness of the model with improvements of over 10% in average AUC when higher-dimensional feature spaces are leveraged. In addition, we show that extreme learning machines can offer clear improvements in terms of training efficiency by reducing the number of trained parameters by a factor of 5 whilst still maintaining the average AUC to within 1.5% of the deep MIL model. Finally, we discuss options of enriching the classical computing framework with quantum algorithms in the future. This work can thus help pave the way towards more accurate and efficient single-cell diagnostics, one of the building blocks of precision medicine.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 70 references that are in the map. First posted on arXiv 13 Mar 2025.

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References · 25

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  10. 2018Barren plateaus in quantum neural network training landscapescited by 630
  11. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  12. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
13 more
  1. 2020Power of data in quantum machine learningcited by 338
  2. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  3. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  4. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
  5. 2022An Aggregation of Aggregation Methods in Computational Pathologycited by 20
  6. 2023On fundamental aspects of quantum extreme learning machinescited by 18
  7. 2021Quantum kernels for real-world predictions based on electronic health recordscited by 15
  8. 2021SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classificationcited by 10
  9. 2024Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspectivecited by 10
  10. 2024Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysiscited by 10
  11. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teachercited by 9
  12. 2024Multi-head Attention-based Deep Multiple Instance Learningcited by 6
  13. 2024M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysiscited by 6

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