Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction

• URL: http://arxiv.org/abs/2406.06517v2
• Date: Mon, 8 Jul 2024 09:52:45 GMT
• Title: Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction
• Authors: Fangliangzi Meng, Hongrun Zhang, Ruodan Yan, Guohui Chuai, Chao Li, Qi Liu,
• Abstract summary: We propose PathoTME, a genomics-guided representation learning framework employing Whole Slide Image (WSI) for pan-cancer TME subtypes prediction. Our model achieves better performance than other state-of-the-art methods across 23 cancer types on TCGA dataset.
• Score: 7.502459517962686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The characterization of Tumor MicroEnvironment (TME) is challenging due to its complexity and heterogeneity. Relatively consistent TME characteristics embedded within highly specific tissue features, render them difficult to predict. The capability to accurately classify TME subtypes is of critical significance for clinical tumor diagnosis and precision medicine. Based on the observation that tumors with different origins share similar microenvironment patterns, we propose PathoTME, a genomics-guided Siamese representation learning framework employing Whole Slide Image (WSI) for pan-cancer TME subtypes prediction. Specifically, we utilize Siamese network to leverage genomic information as a regularization factor to assist WSI embeddings learning during the training phase. Additionally, we employ Domain Adversarial Neural Network (DANN) to mitigate the impact of tissue type variations. To eliminate domain bias, a dynamic WSI prompt is designed to further unleash the model's capabilities. Our model achieves better performance than other state-of-the-art methods across 23 cancer types on TCGA dataset. Our code is available at https://github.com/Mengflz/PathoTME.

Related papers

• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation. Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning. Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
  We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification. Our framework introduces two key components into the common MIL model architecture. We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
  arXiv  Detail & Related papers  (2025-03-08T04:51:58Z)
• A Multi-Modal Deep Learning Framework for Pan-Cancer Prognosis [15.10417643788382]
  In this paper, a deep-learning based model, named UMPSNet, is proposed. UMPSNet integrates four types of important meta data (demographic information, cancer type information, treatment protocols, and diagnosis results) into text templates, and then introduces a text encoder to extract textual features. By incorporating the multi-modality of patient data and joint training, UMPSNet outperforms all SOTA approaches.
  arXiv  Detail & Related papers  (2025-01-13T02:29:42Z)
• Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis [0.15206737182982252]
  PAGET (Pathological image segmentation via AGgrEgated Teachers) is a new knowledge distillation approach that integrates multiple segmentation models. We demonstrate PAGET's ability to perform rapid, comprehensive TME segmentation across various tissue types and medical institutions.
  arXiv  Detail & Related papers  (2025-01-06T10:33:14Z)
• Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images [10.996711454572331]
  Survival prediction, utilizing pathological images and genomic profiles, is increasingly important in cancer analysis and prognosis. Existing multimodal methods often rely on alignment strategies to integrate complementary information. We propose a Multimodal Cross-Task Interaction (MCTI) framework to explore the intrinsic correlations between subtype classification and survival analysis tasks.
  arXiv  Detail & Related papers  (2024-06-25T02:18:35Z)
• Gene-induced Multimodal Pre-training for Image-omic Classification [20.465959546613554]
  This paper proposes a Gene-induced Multimodal Pre-training framework, which jointly incorporates genomics and Whole Slide Images (WSIs) for classification tasks. Experimental results on the TCGA dataset show the superiority of our network architectures and our pre-training framework, achieving 99.47% in accuracy for image-omic classification.
  arXiv  Detail & Related papers  (2023-09-06T04:30:15Z)
• Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction [3.2274401541163322]
  We propose a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens. Our proposed model, SURVPATH, achieves state-of-the-art performance when evaluated against both unimodal and multimodal baselines.
  arXiv  Detail & Related papers  (2023-04-13T21:02:32Z)
• 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)
• Domain Invariant Model with Graph Convolutional Network for Mammogram Classification [49.691629817104925]
  We propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN) We first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN)
  arXiv  Detail & Related papers  (2022-04-21T08:23:44Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Adversarial learning of cancer tissue representations [6.395981404833557]
  We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations. We show that these representations are able to identify a variety of morphological characteristics across three cancer types: Breast, colon, and lung. Our results show that our model captures distinct phenotypic characteristics of real tissue samples, paving the way for further understanding of tumor progression and tumor micro-environment.
  arXiv  Detail & Related papers  (2021-08-04T18:00:47Z)
• Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks [6.427108174481534]
  We present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features. We demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%.
  arXiv  Detail & Related papers  (2021-07-27T19:17:37Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.