Papers Histopathological Image Classification
“Histopathological Image Classification” 태그가 달린 논문 40편 · 필터 해제
FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification
Histopathological image classification constitutes a pivotal task in computer-aided diagnostics. The precise identification and categorization of histopathological images are of paramount significance for early disease d…
DiagnosticHistopathological Image Classificationimage-classificationImage ClassificationHistopathological Image Classification with Cell Morphology Aware Deep Neural Networks
Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recen…
DiagnosticHistopathological Image Classificationimage-classificationImage ClassificationSupervised Contrastive Vision Transformer for Breast Histopathological Image Classification
Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer. Breast tissue histopathological examination is critical in diagnosing and classifying breast cancer. Although existing methods have shown promi…
ClassificationContrastive LearningHistopathological Image Classificationimage-classification+3Focused Active Learning for Histopathological Image Classification
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic se…
Active LearningClassificationHistopathological Image Classificationimage-classification+2A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
Breast cancer classification stands as a pivotal pillar in ensuring timely diagnosis and effective treatment. This study with histopathological images underscores the profound significance of harnessing the synergistic c…
Cancer ClassificationClassificationDiagnosticHistopathological Image Classification+3CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports.…
Contrastive LearningHistopathological Image Classificationimage-classificationImage Classification+2Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT
Deep learning for histopathology has been successfully used for disease classification, image segmentation and more. However, combining image and text modalities using current state-of-the-art (SOTA) methods has been a c…
Caption GenerationDecoderHistopathological Image ClassificationImage Captioning+4Histopathological Image Classification and Vulnerability Analysis using Federated Learning
Healthcare is one of the foremost applications of machine learning (ML). Traditionally, ML models are trained by central servers, which aggregate data from various distributed devices to forecast the results for newly ge…
ClassificationData PoisoningFederated LearningHistopathological Image Classification+3Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medic…
ClassificationHistopathological Image Classificationimage-classificationImage ClassificationSHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
The rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods ha…
Histopathological Image Classificationimage-classificationImage ClassificationSuper-ResolutionBreast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures
Breast cancer is a prevalent form of cancer among women, with over 1.5 million women being diagnosed each year. Unfortunately, the survival rates for breast cancer patients in certain third-world countries, like South Af…
Breast Cancer DetectionData AugmentationHistopathological Image Classificationimage-classification+1Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for…
Deep LearningHistopathological Image ClassificationHistopathological SegmentationImage Generation+4Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels
Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer in tissue samples. Automating this diagnosis poses various issues, most notably caused by the immense image resolution and limit…
Histopathological Image Classificationimage-classificationImage ClassificationMultiple Instance LearningIL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. …
ClassificationHistopathological Image Classificationimage-classificationImage ClassificationDLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions. Previous TTA methods have a commo…
Histopathological Image Classificationimage-classificationImage ClassificationMRI segmentation+1Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of …
Histopathological Image Classificationimage-classificationImage ClassificationImage Segmentation+2Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically si…
Cell SegmentationClassificationContrastive LearningHistopathological Image Classification+5ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which…
Data AugmentationDomain GeneralizationHistopathological Image Classificationimage-classification+2Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnification factors and class imbalance, make …
Histopathological Image Classificationimage-classificationImage ClassificationTripletDiagSet: a dataset for prostate cancer histopathological image classification
Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 millio…
ClassificationGeneral ClassificationHistopathological Image Classificationimage-classification+1