AIMC Topic: Multiple-Instance Learning Algorithms

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Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images.

Medical image analysis May 23, 2025
Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with pat...
Multiple-Instance Learning Algorithms Tertiary Lymphoid Structures Humans Machine Learning Neoplasms Image Interpretation, Computer-Assisted Tumor Microenvironment
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Multiple instance learning-based prediction of programmed death-ligand 1 (PD-L1) expression from hematoxylin and eosin (H&E)-stained histopathological images in breast cancer.

PeerJ Apr 15, 2025
Programmed death-ligand 1 (PD-L1) is an important biomarker increasingly used as a predictive marker in breast cancer immunotherapy. Immunohistochemical quantification remains the standard method for assessment. However, it presents challenges relate...
Reproducibility of Results Breast Neoplasms Biomarkers, Tumor Deep Learning Humans Hematoxylin B7-H1 Antigen Female Immunohistochemistry Staining and Labeling Multiple-Instance Learning Algorithms Eosine Yellowish-(YS)
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M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis.

Medical image analysis Apr 1, 2025
Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers...
Humans Neural Networks, Computer Machine Learning Mutation Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms Neoplasms Image Processing, Computer-Assisted Algorithms
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Multiple Instance Learning-Based Prediction of Blood-Brain Barrier Opening Outcomes Induced by Focused Ultrasound.

IEEE transactions on bio-medical engineering Mar 21, 2025
OBJECTIVE: Targeted blood-brain barrier (BBB) opening using focused ultrasound (FUS) and micro/nanobubbles is a promising method for brain drug delivery. This study aims to explore the feasibility of multiple instance learning (MIL) in accurate and f...
Mice Contrast Media Signal Processing, Computer-Assisted Animals Ultrasonography Sulfur Hexafluoride Multiple-Instance Learning Algorithms Phospholipids Microbubbles Blood-Brain Barrier Drug Delivery Systems Machine Learning
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CoD-MIL: Chain-of-Diagnosis Prompting Multiple Instance Learning for Whole Slide Image Classification.

IEEE transactions on medical imaging Mar 17, 2025
Multiple instance learning (MIL) has emerged as a prominent paradigm for processing the whole slide image with pyramid structure and giga-pixel size in digital pathology. However, existing attention-based MIL methods are primarily trained on the imag...
Humans Machine Learning Natural Language Processing Algorithms Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms
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Entity-level multiple instance learning for mesoscopic histopathology images classification with Bayesian collaborative learning and pathological prior transfer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 27, 2025
BACKGROUND: Entity-level pathologic structures with independent structures and functions are at a mesoscopic scale between the cell-level and slide-level, containing limited structures thus providing fewer instances for multiple instance learning. Th...
Humans Machine Learning Bayes Theorem Multiple-Instance Learning Algorithms Image Interpretation, Computer-Assisted Algorithms
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When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Medical image analysis Jan 14, 2025
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the rece...
Neoplasm Grading Humans Image Interpretation, Computer-Assisted Neoplasms Multiple-Instance Learning Algorithms Deep Learning Image Processing, Computer-Assisted
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The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.

The American journal of pathology Jan 10, 2025
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on me...
Humans Colorectal Neoplasms ROC Curve Mutation Biomarkers, Tumor Supervised Machine Learning Multiple-Instance Learning Algorithms
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Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study.

Computers in biology and medicine Jan 10, 2025
Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its e...
Diagnosis, Computer-Assisted Deep Learning Image Interpretation, Computer-Assisted Humans Multiple-Instance Learning Algorithms Image Processing, Computer-Assisted Algorithms Colorectal Neoplasms
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Acoustic COVID-19 Detection Using Multiple Instance Learning.

IEEE journal of biomedical and health informatics Jan 7, 2025
In the COVID-19 pandemic, a rigorous testing scheme was crucial. However, tests can be time-consuming and expensive. A machine learning-based diagnostic tool for audio recordings could enable widespread testing at low costs. In order to achieve compa...
Signal Processing, Computer-Assisted Multiple-Instance Learning Algorithms COVID-19 Algorithms SARS-CoV-2 Humans Machine Learning
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