AIMC Topic: Multiple-Instance Learning Algorithms

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FR-MIL: Distribution Re-Calibration-Based Multiple Instance Learning With Transformer for Whole Slide Image Classification.

IEEE transactions on medical imaging Jan 2, 2025
In digital pathology, whole slide images (WSI) are crucial for cancer prognostication and treatment planning. WSI classification is generally addressed using multiple instance learning (MIL), alleviating the challenge of processing billions of pixels...
Neoplasms Humans Multiple-Instance Learning Algorithms Machine Learning Image Interpretation, Computer-Assisted Algorithms
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Weakly Supervised Multiple Instance Learning Model With Generalization Ability for Clinical Adenocarcinoma Screening on Serous Cavity Effusion Pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Nov 6, 2024
Accurate and rapid screening of adenocarcinoma cells in serous cavity effusion is vital in diagnosing the stage of metastatic tumors and providing prompt medical treatment. However, it is often difficult for pathologists to screen serous cavity effus...
Supervised Machine Learning Pleural Effusion, Malignant Deep Learning Multiple-Instance Learning Algorithms Humans Adenocarcinoma
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A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation.

Computers in biology and medicine Jul 1, 2025
BACKGROUND: Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpret...
Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms Neoplasms Cluster Analysis Humans Machine Learning
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AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes.

Medical image analysis Jul 1, 2025
Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing gigapixel-resolution images with slide-level labels. Recent attention-based MIL architectures have significa...
Algorithms Image Interpretation, Computer-Assisted Humans Machine Learning Multiple-Instance Learning Algorithms
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S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning.

Computers in biology and medicine Jun 1, 2025
Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakl...
Cell Nucleus Multiple-Instance Learning Algorithms Deep Learning Image Processing, Computer-Assisted Humans Algorithms
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Whole slide image-level classification of malignant effusion cytology using clustering-constrained attention multiple instance learning.

Lung cancer (Amsterdam, Netherlands) Jun 1, 2025
BACKGROUND: Cytological diagnosis of pleural effusion plays an important role in the early detection and diagnosis of lung cancers. Recently, attempts have been made to overcome low diagnostic accuracy and interobserver variability using artificial i...
Cluster Analysis Image Processing, Computer-Assisted Cytodiagnosis Pleural Effusion, Malignant Lung Neoplasms Humans Multiple-Instance Learning Algorithms
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Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning.

Computers in biology and medicine Jun 1, 2025
Brain tumors have a great impact on patients' quality of life and accurate histopathological classification of brain tumors is crucial for patients' prognosis. Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide i...
Brain Neoplasms Supervised Machine Learning Humans Image Interpretation, Computer-Assisted Algorithms Multiple-Instance Learning Algorithms
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Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

IEEE journal of biomedical and health informatics Jun 1, 2025
Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for ...
Databases, Factual Brain Image Interpretation, Computer-Assisted Humans Brain Neoplasms Multiple-Instance Learning Algorithms Machine Learning Glioma
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Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.

Cell systems Mar 19, 2025
The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understan...
Melanoma Drug Discovery Deep Learning Imaging, Three-Dimensional Humans Cell Shape Multiple-Instance Learning Algorithms
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CAMIL: channel attention-based multiple instance learning for whole slide image classification.

Bioinformatics (Oxford, England) Feb 4, 2025
MOTIVATION: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple instance learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel...
Multiple-Instance Learning Algorithms Image Processing, Computer-Assisted Computational Biology Machine Learning Humans Algorithms
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