Multiple Instance Learning
234 papers with code • 0 benchmarks • 8 datasets
Multiple Instance Learning is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\{x_1,x_2, \ldots,x_M\}$, and there is one single label $Y$ per bag, $Y\in\{0, 1\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.
Source: Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Benchmarks
These leaderboards are used to track progress in Multiple Instance Learning
Libraries
Use these libraries to find Multiple Instance Learning models and implementations
UCF-Crime
AVE
SPOT
CamNuvem Dataset
Latest papers
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision.
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification
Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches.
BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
hust-linyi/bonus
•
•
To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei.
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
dootmaan/icmil
•
•
Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost.
Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
scjjb/ovarian_subtype_mags
•
•
Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear.
High-resolution Image-based Malware Classification using Multiple Instance Learning
timppeters/mil-malware-images
•
•
This paper proposes a novel method of classifying malware into families using high-resolution greyscale images and multiple instance learning to overcome adversarial binary enlargement.
Inherently Interpretable Time Series Classification via Multiple Instance Learning
jaearly/miltimeseriesclassification
•
•
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes.
Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting.
Mixed Models with Multiple Instance Learning
AIH-SGML/mixmil
•
•
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease.
