Multiple Instance Learning
235 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
Most implemented papers
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
binli123/dsmil-wsi
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CVPR 2021
We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations.
DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learning
DEEMD can be explored for use on other emerging viruses and datasets to rapidly identify candidate antiviral treatments in the future}.
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection
Nahid1992/CAD_PE
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At the image level, we compare convolutional neural networks (CNNs) with vision transformers, and contrast self-supervised learning (SSL) with supervised learning, followed by an evaluation of transfer learning compared with training from scratch.
Explainable multiple abnormality classification of chest CT volumes
rachellea/explainable-ct-ai
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We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images, in which a model must indicate the regions used to predict each abnormality.
Object Localization under Single Coarse Point Supervision
ucas-vg/pointtinybenchmark
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CVPR 2022
In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
dearcaat/rrt-mil
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Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R$^2$T is tailored to re-embed instance features online.
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears.
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Fully Convolutional Multi-Class Multiple Instance Learning
We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network.
Simpler non-parametric methods provide as good or better results to multiple-instance learning.
Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances).
