Learnable Context in Multiple Instance Learning for Whole Slide Image Classification and Segmentation.
Multiple instance learning (MIL) has become a cornerstone in whole slide image (WSI) analysis. In this paradigm, a WSI is conceptualized as a bag of instances. Instance features are extracted by a feature extractor, and then a feature aggregator fuses these instance features into a bag representatio...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2322 - 2337 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278948&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278948 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278948 187278948 187278948 10.1007/s10278-024-01302-8 187278948 ppf: 2322 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Learnable Context in Multiple Instance Learning for Whole Slide Image Classification and Segmentation. aug: au: Huang, Yu-Yuan Chu, Wei-Ta affil: https://ror.org/01b8kcc49 National Cheng Kung University, Tainan, Taiwan sug: subj: Deep Learning Classification Diagnostic Imaging Image Processing, Computer Assisted Human Funding Source Neoplasms Pathology Bioinformatics Neural Networks (Computer) Machine Learning Microscopy, Virtual Diagnosis, Computer Assisted Conceptual Framework Descriptive Statistics Paired T-Tests Ablation Techniques ab: Multiple instance learning (MIL) has become a cornerstone in whole slide image (WSI) analysis. In this paradigm, a WSI is conceptualized as a bag of instances. Instance features are extracted by a feature extractor, and then a feature aggregator fuses these instance features into a bag representation. In this paper, we advocate that both feature extraction and aggregation can be enhanced by considering the context or correlation between instances. We learn contextual features between instances, and then fuse contextual features with instance features to enhance instance representations. For feature aggregation, we observe performance instability particularly when disease-positive instances are only a minor fraction of the WSI. We introduce a self-attention mechanism to discover correlation among instances and foster more effective bag representations. Through comprehensive testing, we have demonstrated that the proposed method outperforms existing WSI classification methods by 1 to 4% classification accuracy, based on the Camelyon16 and the TCGA-NSCLC datasets. The proposed method also outperforms the most recent weakly supervised WSI segmentation method by 0.6 in terms of the Dice coefficient, based on the Camelyon16 dataset. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|