SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images

• URL: http://arxiv.org/abs/2407.00664v2
• Date: Tue, 22 Oct 2024 03:34:43 GMT
• Title: SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images
• Authors: Zekang Yang, Hong Liu, Xiangdong Wang,
• Abstract summary: Existing methods for cancer survival prediction based on Whole Slide Image (WSI) often fail to provide better clinically meaningful predictions. We propose a Sparse Context-aware Multiple Instance Learning framework for predicting cancer survival probability distributions. Our experimental results indicate that SCMIL outperforms current state-of-the-art methods for survival prediction, offering more clinically meaningful and interpretable outcomes.
• Score: 9.005219442274344
• License:
• Abstract: Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI). Previous methods cannot effectively capture the intricate interaction features among instances within the local area of WSI. Moreover, existing methods for cancer survival prediction based on WSI often fail to provide better clinically meaningful predictions. To overcome these challenges, we propose a Sparse Context-aware Multiple Instance Learning (SCMIL) framework for predicting cancer survival probability distributions. SCMIL innovatively segments patches into various clusters based on their morphological features and spatial location information, subsequently leveraging sparse self-attention to discern the relationships between these patches with a context-aware perspective. Considering many patches are irrelevant to the task, we introduce a learnable patch filtering module called SoftFilter, which ensures that only interactions between task-relevant patches are considered. To enhance the clinical relevance of our prediction, we propose a register-based mixture density network to forecast the survival probability distribution for individual patients. We evaluate SCMIL on two public WSI datasets from the The Cancer Genome Atlas (TCGA) specifically focusing on lung adenocarcinom (LUAD) and kidney renal clear cell carcinoma (KIRC). Our experimental results indicate that SCMIL outperforms current state-of-the-art methods for survival prediction, offering more clinically meaningful and interpretable outcomes. Our code is accessible at https://github.com/yang-ze-kang/SCMIL.

Related papers

• Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development [59.74920439478643]
  In this paper, we collect and annotated the first benchmark dataset that covers diverse ERUS scenarios. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. We introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR)
  arXiv  Detail & Related papers  (2024-08-19T15:04:42Z)
• SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival [8.403756148610269]
  Multimodal prediction of cancer patient survival offers a more comprehensive and precise approach. This paper introduces SELECTOR, a heterogeneous graph-aware network based on convolutional mask encoders. Our method significantly outperforms state-of-the-art methods in both modality-missing and intra-modality information-confirmed cases.
  arXiv  Detail & Related papers  (2024-03-14T11:23:39Z)
• Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction [43.1748594898772]
  We propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colon-related cancer survival prediction. We emphasize the unsupervised pretraining to capture the intrinsic interaction between tissue microenvironments in gigapixel whole slide images. We evaluate our approach on both TCGA colon and rectum cancer cohorts, showing that the proposed approach is competitive and outperforms state-of-the-art studies.
  arXiv  Detail & Related papers  (2023-07-22T00:59:26Z)
• Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer [10.994223928445589]
  We propose a merging-diverging learning framework for survival prediction from multi-modality images. This framework has a merging encoder to fuse multi-modality information and a diverging decoder to extract region-specific information. Our framework is demonstrated on survival prediction from PET-CT images in Head and Neck (H&N) cancer.
  arXiv  Detail & Related papers  (2023-07-07T07:16:03Z)
• Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning [48.02011627390706]
  We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
  arXiv  Detail & Related papers  (2023-03-02T15:18:58Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• Colorectal cancer survival prediction using deep distribution based multiple-instance learning [5.231498575799198]
  We develop a distribution based multiple-instance survival learning algorithm (DeepDisMISL) Our results suggest that the more information about the distribution of the patch scores for a WSI, the better is the prediction performance. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-04-24T14:55:57Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images [83.7047542725469]
  Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming the state-of-the-art alternatives, even while learning from a single slide.
  arXiv  Detail & Related papers  (2021-09-22T15:06:06Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks [38.39901070720532]
  Current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs) We propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling. We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets.
  arXiv  Detail & Related papers  (2020-09-23T14:31:15Z)

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.