Papers Weakly Supervised Classification
“Weakly Supervised Classification” 태그가 달린 논문 40편 · 필터 해제
Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influen…
Weakly Supervised ClassificationPartial Label LearningDSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification
Whole-slide images (WSIs) are critical for cancer diagnosis due to their ultra-high resolution and rich semantic content. However, their massive size and the limited availability of fine-grained annotations pose substant…
image-classificationImage ClassificationWeakly Supervised Classificationwhole slide imagesA Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Multiple instance learning (MIL) is a promising approach for weakly supervised classification in pathology using whole slide images (WSIs). However, conventional MIL methods such as Attention-Based Deep Multiple Instance…
Computational EfficiencyMultiple Instance LearningWeakly Supervised Classificationwhole slide imagesMSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
Multiple instance learning (MIL) has become a standard paradigm for weakly supervised classification of whole slide images (WSI). However, this paradigm relies on the use of a large number of labelled WSIs for training. …
image-classificationImage ClassificationLanguage ModellingLarge Language Model+3Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarc…
ClassificationSelf-Supervised LearningWeakly Supervised ClassificationRoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification
Whole slide image (WSI) classification is a critical task in computational pathology. However, the gigapixel-size of such images remains a major challenge for the current state of deep-learning. Current methods rely on m…
image-classificationImage ClassificationMultiple Instance LearningPosition+1PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to their weak supervision nature, the MIL metho…
Multiple Instance LearningWeakly Supervised ClassificationWeakly-supervised LearningWeakly Supervised Object Detection+1CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference. At the initialization stage, we take fu…
Classificationimage-classificationImage ClassificationMulti-Label Image Classification+2Kernel Density Matrices for Probabilistic Deep Learning
This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and disc…
Deep LearningDensity Estimationimage-classificationImage Classification+2CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images
The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes. However, existing attention…
DiagnosticImage ClassificationMultiple Instance LearningMultiple Instance LearningMultiple Instance Learning+2Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning
Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process. We propose Discriminative Region Active Samp…
ClassificationCPUGPUMultiple Instance Learning+2Easy Learning from Label Proportions
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class labels at each bag is available. Albeit,…
Weakly Supervised ClassificationFastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification
Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tas…
Classificationtext-classificationText ClassificationWeakly Supervised ClassificationPositive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
The need to learn from positive and unlabeled data, or PU learning, arises in many applications and has attracted increasing interest. While random forests are known to perform well on many tasks with positive and negati…
Feature ImportanceWeakly Supervised ClassificationLIME: Weakly-Supervised Text Classification Without Seeds
In weakly-supervised text classification, only label names act as sources of supervision. Predominant approaches to weakly-supervised text classification utilize a two-phase framework, where test samples are first assign…
ClassificationNatural Language Inferencetext-classificationText Classification+1Label Propagation with Weak Supervision
Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysi…
Weakly Supervised ClassificationWeakly-supervised LearningClass-Imbalanced Complementary-Label Learning via Weighted Loss
Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. In such scenarios…
Multi-class ClassificationWeakly Supervised ClassificationWeakly Supervised Classification of Vital Sign Alerts as Real or Artifact
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attemp…
Weakly Supervised ClassificationLearning from Label Proportions by Learning with Label Noise
Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels. Th…
Weakly Supervised ClassificationImportance of Textlines in Historical Document Classification
This paper describes a system prepared at Brno University of Technology for ICDAR 2021 Competition on Historical Document Classification, experiments leading to its design, and the main findings. The solved tasks include…
ClassificationDocument ClassificationWeakly Supervised Classification