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
- 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 Facial Features Estimation [0.0]
SLYKLatent is a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets.
Our evaluation on benchmark datasets achieves an 8.7% improvement on Gaze360, rivals top MPIIFaceGaze results, and leads on a subset of ETH-XGaze by 13%.
arXiv Detail & Related papers (2024-02-02T16:47:18Z) - BAL: Balancing Diversity and Novelty for Active Learning [53.289700543331925]
We introduce a novel framework, Balancing Active Learning (BAL), which constructs adaptive sub-pools to balance diverse and uncertain data.
Our approach outperforms all established active learning methods on widely recognized benchmarks by 1.20%.
arXiv Detail & Related papers (2023-12-26T08:14:46Z) - Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning [50.809769498312434]
We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS)
Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
arXiv Detail & Related papers (2023-11-22T03:45:30Z) - Multi-Task Self-Supervised Time-Series Representation Learning [3.31490164885582]
Time-series representation learning can extract representations from data with temporal dynamics and sparse labels.
We propose a new time-series representation learning method by combining the advantages of self-supervised tasks.
We evaluate the proposed framework on three downstream tasks: time-series classification, forecasting, and anomaly detection.
arXiv Detail & Related papers (2023-03-02T07:44:06Z) - 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) - 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.