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 401–450 of 744 papers

TitleStatusHype
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
WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need—0
Weak to Strong Learning from Aggregate Labels—0
Whole Slide Image Classification of Salivary Gland Tumours—0
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis—0
Multiple Instance Verification—0
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information—0
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging—0
A convex method for classification of groups of examples—0
Action Representation Using Classifier Decision Boundaries—0
Active Deep Multiple Instance Learning—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—0
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology—0
Address Instance-level Label Prediction in Multiple Instance Learning—0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
A Feature Selection Method for Multivariate Performance Measures—0
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis—0
AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients—0
Multiple instance learning for sequence data with across bag dependencies—0
A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images—0
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning—0
A Multi-scale Multiple Instance Video Description Network—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
An Aggregation of Aggregation Methods in Computational Pathology—0
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography—0
An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI—0
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective—0
A new Time-decay Radiomics Integrated Network (TRINet) for short-term breast cancer risk prediction—0
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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