AIMC Topic: Multiple-Instance Learning Algorithms

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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 Humans Algorithms SARS-CoV-2 Machine Learning Multiple-Instance Learning Algorithms COVID-19
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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...
Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms Humans Algorithms Machine Learning Neoplasms
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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 Humans Deep Learning Multiple-Instance Learning Algorithms Pleural Effusion, Malignant Adenocarcinoma
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[Feature distillation multiple instance learning method based on sequence reorganized Mamba].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Dec 25, 2025
Prostate cancer is one of the most prevalent malignancies among men worldwide, and its diagnosis relies heavily on accurate analysis of whole slide imaging (WSI) in histopathology. However, manual interpretation is time-consuming and prone to inconsi...
Machine Learning Algorithms Humans Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms Male Prostatic Neoplasms
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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 Aug 1, 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...
Tertiary Lymphoid Structures Tumor Microenvironment Neoplasms Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms Humans Machine Learning
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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...
Neoplasms Cluster Analysis Image Interpretation, Computer-Assisted Multiple-Instance Learning Algorithms 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...
Multiple-Instance Learning Algorithms Algorithms Image Interpretation, Computer-Assisted Humans Machine Learning
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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...
Humans Cluster Analysis Cytodiagnosis Image Processing, Computer-Assisted Lung Neoplasms Pleural Effusion, Malignant 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...
Humans Algorithms Brain Neoplasms Image Interpretation, Computer-Assisted Supervised Machine Learning Multiple-Instance Learning Algorithms
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