SOTAVerified

Multiple Instance Learning

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

Papers

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Showing 701–744 of 744 papers

TitleStatusHype
Simpler non-parametric methods provide as good or better results to multiple-instance learning.Code0
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning—0
Semantic Component Analysis—0
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection—0
Multiple-Instance Learning: Radon-Nikodym Approach to Distribution Regression Problem—0
Multiple--Instance Learning: Christoffel Function Approach to Distribution Regression Problem—0
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning—0
Multiple Instance Dictionary Learning using Functions of Multiple InstancesCode0
Estimating Target Signatures with Diverse Density—0
Relaxed Multiple-Instance SVM with Application to Object Discovery—0
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography—0
Quantity, Contrast, and Convention in Cross-Situated Language Comprehension—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—0
Multiple Instance Learning for Soft Bags via Top Instances—0
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning—0
Deep Multiple Instance Learning for Image Classification and Auto-Annotation—0
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection—0
A Multi-scale Multiple Instance Video Description Network—0
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Instance Significance Guided Multiple Instance Boosting for Robust Visual Tracking—0
Fully Convolutional Multi-Class Multiple Instance LearningCode0
Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and L_1 Regularization—0
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning—0
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection—0
From Image-level to Pixel-level Labeling with Convolutional Networks—0
From Captions to Visual Concepts and BackCode0
Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis—0
Feature and Region Selection for Visual Learning—0
Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data—0
Multi-fold MIL Training for Weakly Supervised Object Localization—0
Confidence-Rated Multiple Instance Boosting for Object Detection—0
MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation—0
Classroom Video Assessment and Retrieval via Multiple Instance Learning—0
Dissimilarity-based Ensembles for Multiple Instance Learning—0
Generative Multiple-Instance Learning Models For Quantitative Electromyography—0
Multiple Instance Learning by Discriminative Training of Markov Networks—0
Multiple Instance Learning with Bag Dissimilarities—0
Two-person interaction detection using body-pose features and multiple instance learning—0
Multiple Instance Learning on Structured Data—0
Multiple Instance Filtering—0
A Feature Selection Method for Multivariate Performance Measures—0
Convex Multiple-Instance Learning by Estimating Likelihood Ratio—0
Multiple-Instance Pruning For Learning Efficient Cascade Detectors—0
Support Vector Machines for Multiple-Instance Learning—0
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All datasetsCAMELYON16TCGAElephantMusk v1Musk v2

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Snuffy (DINO Exhaustive)AUC0.99—Unverified
2Snuffy (SimCLR Exhaustive)AUC0.97—Unverified
3CAMILAUC0.96—Unverified
4CAMIL (CAMIL-L)AUC0.95—Unverified
5CAMIL (CAMIL-G)AUC0.95—Unverified
6DTFD-MIL (AFS)AUC0.95—Unverified
7DTFD-MIL (MAS)AUC0.95—Unverified
8DTFD-MIL (MaxMinS)AUC0.94—Unverified
9TransMILAUC0.93—Unverified
10DSMIL-LCAUC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DTFD-MIL (MAS)AUC0.96—Unverified
2DTFD-MIL (AFS)ACC0.95—Unverified
3Snuffy (SimCLR Exhaustive)ACC0.95—Unverified
4DSMIL-LCACC0.93—Unverified
5DSMILACC0.92—Unverified
6DTFD-MIL (MaxMinS)ACC0.89—Unverified
7TransMILACC0.88—Unverified
8DTFD-MIL (MaxS)ACC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyAUC0.97—Unverified
2DSMILACC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyACC0.96—Unverified
2DSMILACC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1DSMILACC0.93—Unverified
2SnuffyACC0.79—Unverified