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 651–700 of 744 papers

TitleStatusHype
MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification—0
MMIL: A novel algorithm for disease associated cell type discovery—0
Model-Based Multiple Instance Learning—0
Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection—0
Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection—0
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification—0
Motion-Aware Feature for Improved Video Anomaly Detection—0
MSA-MIL: A deep residual multiple instance learning model based on multi-scale annotation for classification and visualization of glomerular spikes—0
MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification—0
Multi-Attention Multiple Instance Learning—0
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events—0
Multi-fold MIL Training for Weakly Supervised Object Localization—0
Multi-Instance Learning for End-to-End Knowledge Base Question Answering—0
Multi-Instance Multi-Scale CNN for Medical Image Classification—0
Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and Omics Data for Precision Oncology—0
Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio—0
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection—0
Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms—0
Multiple Instance Filtering—0
Multiple Instance Fuzzy Inference Neural Networks—0
Multiple Instance Hybrid Estimator for Learning Target Signatures—0
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection—0
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers—0
Multiple Instance Learning by Discriminative Training of Markov Networks—0
Multiple--Instance Learning: Christoffel Function Approach to Distribution Regression Problem—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data—0
Multiple Instance Learning for Cheating Detection and Localization in Online Examinations—0
Multiple Instance Learning for Content Feedback Localization without Annotation—0
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets—0
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential—0
Multiple Instance Learning for ECG Risk Stratification—0
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices—0
Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Multiple Instance Learning for Soft Bags via Top Instances—0
Multiple Instance Learning for Uplift Modeling—0
Multiple Instance Learning on Structured Data—0
Multiple-Instance Learning: Radon-Nikodym Approach to Distribution Regression Problem—0
Multiple Instance Learning with Bag Dissimilarities—0
LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation—0
Multiple instance learning with graph neural networks—0
Multiple Instance Learning with random sampling for Whole Slide Image Classification—0
Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric—0
Multiple Instance Learning with Trainable Decision Tree Ensembles—0
Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences—0
Multiple-Instance Pruning For Learning Efficient Cascade Detectors—0
An attention-based multi-resolution model for prostate whole slide imageclassification and localization—0
Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data—0
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification—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