Papers Histopathological Image Classification
“Histopathological Image Classification” 태그가 달린 논문 40편 · 필터 해제
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer imag…
Adversarial AttackGeneral ClassificationHistopathological Image ClassificationImage Classification+1Self-supervised driven consistency training for annotation efficient histopathology image analysis
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter…
Histopathological Image ClassificationRepresentation LearningSelf-Supervised LearningUniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterizatio…
Colorectal Polyps CharacterizationGeneral ClassificationHistopathological Image ClassificationManagement+1Magnification Generalization for Histopathology Image Embedding
Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is …
Breast Cancer Histology Image ClassificationClassification Of Breast Cancer Histology ImagesDomain AdaptationDomain Generalization+2MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis
Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into…
Histopathological Image Classificationimage-classificationImage ClassificationC-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification
Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging…
ClassificationGeneral ClassificationHistopathological Image Classificationimage-classification+2Multiple Instance Learning with Center Embeddings for Histopathology Classification
Histopathology image analysis plays an important role in the treatment and diagnosis of cancer. However, analysis of whole slide images (WSI) with deep learning is challenging given that the curation of pixel-level annot…
ClassificationGeneral ClassificationHistopathological Image ClassificationMultiple Instance Learning+2Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics
Annotating histopathological images is a time-consuming andlabor-intensive process, which requires broad-certificated pathologistscarefully examining large-scale whole-slide images from cells to tissues.Recent frontiers …
Contrastive LearningGraph Neural NetworkHistopathological Image Classificationimage-classification+3HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local representations (patch-wise) before combining the…
Histopathological Image Classificationimage-classificationImage ClassificationBatch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
Variants of Triplet networks are robust entities for learning a discriminative embedding subspace. There exist different triplet mining approaches for selecting the most suitable training triplets. Some of these mining m…
Dimensionality ReductionHistopathological Image ClassificationMetric LearningTripletOffline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, bot…
Dimensionality ReductionHistopathological Image ClassificationMetric LearningTripletHACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the str…
DiagnosticGeneral ClassificationGraph Neural NetworkHistopathological Image Classification+3Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution
Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively …
feature selectionGeneral ClassificationGenerative Adversarial NetworkHistopathological Image Classification+5Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representat…
Dimensionality ReductionDomain GeneralizationFew-Shot LearningHistopathological Image Classification+4Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any…
Classification Of Breast Cancer Histology ImagesDimensionality ReductionDomain GeneralizationHistopathological Image Classification+2Texture CNN for Histopathological Image Classification
Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variabi…
ClassificationGeneral ClassificationHistopathological Image Classificationimage-classification+1Regression Concept Vectors for Bidirectional Explanations in Histopathology
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making. In this work, we pro…
Breast Cancer DetectionBreast Cancer Histology Image ClassificationDecision MakingHistopathological Image Classification+2Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement be…
Breast Cancer DetectionBreast Cancer Histology Image ClassificationClassificationDiagnostic+7A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours
Tissue texture is known to exhibit a heterogeneous or non-stationary nature, therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the…
ClassificationDiagnosticGeneral ClassificationHistopathological Image Classification+2DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In …
ClassificationDictionary LearningDiversityGeneral Classification+3