Multiple Instance Learning with random sampling for Whole Slide Image Classification

• URL: http://arxiv.org/abs/2403.05351v1
• Date: Fri, 8 Mar 2024 14:31:40 GMT
• Title: Multiple Instance Learning with random sampling for Whole Slide Image Classification
• Authors: H. Keshvarikhojasteh, J.P.W. Pluim, M. Veta
• Abstract summary: Random sampling of patches during training is computationally efficient and serves as a regularization strategy. We find optimal performance enhancement of 1.7% using thirty percent of patches on the CAMELYON16 dataset, and 3.7% with only eight samples on the TUPAC16 dataset. We also find interpretability effects are strongly dataset-dependent, with interpretability impacted on CAMELYON16, while remaining unaffected on TUPAC16.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In computational pathology, random sampling of patches during training of Multiple Instance Learning (MIL) methods is computationally efficient and serves as a regularization strategy. Despite its promising benefits, questions concerning performance trends for varying sample sizes and its influence on model interpretability remain. Addressing these, we reach an optimal performance enhancement of 1.7% using thirty percent of patches on the CAMELYON16 dataset, and 3.7% with only eight samples on the TUPAC16 dataset. We also find interpretability effects are strongly dataset-dependent, with interpretability impacted on CAMELYON16, while remaining unaffected on TUPAC16. This reinforces that both the performance and interpretability relationships with sampling are closely task-specific. End-to-end training with 1024 samples reveals improvements across both datasets compared to pre-extracted features, further highlighting the potential of this efficient approach.

Related papers

• Dynamic Influence Tracker: Measuring Time-Varying Sample Influence During Training [4.423200604919468]
  We propose Dynamic Influence Tracker (DIT), which captures the time-varying sample influence across arbitrary time windows during training. DIT offers three key insights: 1) Samples show different time-varying influence patterns, with some samples important in the early training stage while others become important later.
  arXiv  Detail & Related papers  (2025-02-15T13:24:21Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples from the entire dataset. We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Dataset Quantization with Active Learning based Adaptive Sampling [11.157462442942775]
  We show that maintaining performance is feasible even with uneven sample distributions. We propose a novel active learning based adaptive sampling strategy to optimize the sample selection. Our approach outperforms the state-of-the-art dataset compression methods.
  arXiv  Detail & Related papers  (2024-07-09T23:09:18Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Task-oriented Embedding Counts: Heuristic Clustering-driven Feature Fine-tuning for Whole Slide Image Classification [1.292108130501585]
  We propose a clustering-driven feature fine-tuning method (HC-FT) to enhance the performance of multiple instance learning. The proposed method is evaluated on both CAMELYON16 and BRACS datasets, achieving an AUC of 97.13% and 85.85%, respectively.
  arXiv  Detail & Related papers  (2024-06-02T08:53:45Z)
• SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning [0.0]
  We present SLYKLatent, a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets. SLYKLatent utilizes Self-Supervised Learning for initial training with facial expression datasets, followed by refinement with a patch-based tri-branch network. Our evaluation on benchmark datasets achieves a 10.9% improvement on Gaze360, supersedes top MPIIFaceGaze results with 3.8%, and leads on a subset of ETH-XGaze by 11.6%.
  arXiv  Detail & Related papers  (2024-02-02T16:47:18Z)
• On Modality Bias Recognition and Reduction [70.69194431713825]
  We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
  arXiv  Detail & Related papers  (2022-02-25T13:47:09Z)
• Improving Robustness and Efficiency in Active Learning with Contrastive Loss [13.994967246046008]
  This paper introduces supervised contrastive active learning (SCAL) by leveraging the contrastive loss for active learning in a supervised setting. We propose efficient query strategies in active learning to select unbiased and informative data samples of diverse feature representations.
  arXiv  Detail & Related papers  (2021-09-13T21:09:21Z)
• To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection [95.21700830273221]
  Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. We propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions. We prove that even a much smaller dataset with well-matched annotations can facilitate models to achieve better performance as well as generalizability.
  arXiv  Detail & Related papers  (2021-09-04T02:45:22Z)
• Rethinking Sampling Strategies for Unsupervised Person Re-identification [59.47536050785886]
  We analyze the reasons for the performance differences between various sampling strategies under the same framework and loss function. Group sampling is proposed, which gathers samples from the same class into groups. Experiments on Market-1501, DukeMTMC-reID and MSMT17 show that group sampling achieves performance comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-07-07T05:39:58Z)
• Federated Learning under Importance Sampling [49.17137296715029]
  We study the effect of importance sampling and devise schemes for sampling agents and data non-uniformly guided by a performance measure. We find that in schemes involving sampling without replacement, the performance of the resulting architecture is controlled by two factors related to data variability at each agent.
  arXiv  Detail & Related papers  (2020-12-14T10:08:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.