mlsanityData to 14 Oct 2026

Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

arXiv 2306.10805 ↗First posted 19 Jun 2023physics.med-ph cs.CV eess.IVcited by 2

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Abstract (arXiv)

In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard treatment option for patients who have locally advanced cancer and some large operable tumors. A patient will have better prognosis when he has achieved a pathological complete response (pCR) with the treatment of NAC. There has been a trend to directly predict pCR to NAC from histological images based on deep learning (DL). However, the DL-based predictive models numerically have better performances in internal validation than in external validation. In this paper, we aim to alleviate this situation with an intrinsic approach. We propose an experts' cognition-driven ensemble deep learning (ECDEDL) approach. Taking the cognition of both pathology and artificial intelligence experts into consideration to improve the generalization of the predictive model to the external validation, ECDEDL can intrinsically approximate the working paradigm of a human being which will refer to his various working experiences to make decisions. ECDEDL was validated with 695 WSIs collected from the same center as the primary dataset to develop the predictive model and perform the internal validation, and was also validated with 340 WSIs collected from other three centers as the external dataset to perform the external validation. In external validation, ECDEDL improves the AUCs of pCR prediction from 61.52(59.80-63.26) to 67.75(66.74-68.80) and the Accuracies of pCR prediction from 56.09(49.39-62.79) to 71.01(69.44-72.58). ECDEDL was quite effective for external validation of predicting pCR to NAC from histological images in breast cancer, numerically approximating the internal validation.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 64 references that are in the map. First posted on arXiv 19 Jun 2023.

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23 of 64 references.

References · 23

  1. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  2. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  4. 2021Transformers in Vision: A Surveycited by 1,013
  5. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  6. 2021Domain Generalization: A Surveycited by 764
  7. 2020A Survey on Visual Transformercited by 654
  8. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 352
  9. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  10. 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 213
  11. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  12. 2020Domain Adaptive Ensemble Learningcited by 156
11 more
  1. 2020Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularizationcited by 123
  2. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 119
  3. 2020On the Value of Out-of-Distribution Testing: An Example of Goodhart's Lawcited by 74
  4. 2021Adaptive Methods for Real-World Domain Generalizationcited by 66
  5. 2018Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networkscited by 57
  6. 2018Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Imagescited by 56
  7. 2020Deep learning-based transformation of the H&E stain into special stainscited by 55
  8. 2021A Survey on Ensemble Learning under the Era of Deep Learningcited by 47
  9. 2021Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentationcited by 33
  10. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 11
  11. 2021One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancercited by 3

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024Introducing 'Inside' Out of Distributioncited by 1
  2. 2023Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels: An Application Study on Tumour Segmentation for Breast Cancercited by 0