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 501–550 of 744 papers

TitleStatusHype
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation—0
Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images—0
Towards end-to-end disease prediction from raw metagenomic data—0
Towards Hierarchical Regional Transformer-based Multiple Instance Learning—0
Towards Open Set Video Anomaly Detection—0
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation—0
Towards Universal Representation for Unseen Action Recognition—0
Training image classifiers using Semi-Weak Label Data—0
Training Medical Image Analysis Systems like Radiologists—0
Training object class detectors with click supervision—0
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network—0
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection—0
Two-person interaction detection using body-pose features and multiple instance learning—0
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms—0
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data—0
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography—0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos—0
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification—0
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection—0
User Personalized Satisfaction Prediction via Multiple Instance Deep Learning—0
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning—0
Using Multiple Instance Learning to Build Multimodal Representations—0
Utilizing the Instability in Weakly Supervised Object Detection—0
Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions—0
Video Representation Learning Using Discriminative Pooling—0
Video Segmentation via Multiple Granularity Analysis—0
VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction—0
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection—0
Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision—0
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling—0
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network—0
Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification—0
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health—0
Weakly-Supervised Audio-Visual Video Parsing with Prototype-based Pseudo-Labeling—0
Weakly Supervised Cascaded Convolutional Networks—0
Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups—0
Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images—0
Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency—0
Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM—0
Weakly Supervised Object Detection with Segmentation Collaboration—0
Weakly Supervised Object Localization Using Things and Stuff Transfer—0
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Weakly Supervised Object Localization With Progressive Domain Adaptation—0
Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events—0
Weakly Supervised Scalable Audio Content Analysis—0
Weakly Supervised Segmentation of Hyper-Reflective Foci with Compact Convolutional Transformers and SAM2—0
Weakly-Supervised Trajectory Segmentation for Learning Reusable Skills—0
Weakly Supervised Universal Fracture Detection in Pelvic X-rays—0
Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction—0
Weak-Shot Object Detection Through Mutual Knowledge Transfer—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