Results 261 to 270 of about 4,840,174 (316)
Some of the next articles are maybe not open access.
Computers & electrical engineering, 2021
Quantifying mitosis in pathological sections is of great significance in the pathological diagnosis of breast cancer as it is used to evaluate the aggressiveness of the tumor and to provide more comprehensive and reliable information for accurate ...
Xipeng Pan +6 more
semanticscholar +1 more source
Quantifying mitosis in pathological sections is of great significance in the pathological diagnosis of breast cancer as it is used to evaluate the aggressiveness of the tumor and to provide more comprehensive and reliable information for accurate ...
Xipeng Pan +6 more
semanticscholar +1 more source
Mitosis detection on histopathological images using statistical detection algorithms
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015In this work, the utility and accuracy of the statistical detection algorithms for the detection of mitosis on histopathological images have been investigated. In the first stage, the subset images involving mitotic cells from the original images have been created. The occurance based texture filters have been applied to each subset image.
Mustafa Ustuner, Gokhan Bilgin
openaire +1 more source
Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
Histopathological image analysis is a pivotal area of medical research that leverages deep learning to derive quantitative insights from Hematoxylin and Eosin (H\&E) stained images.
Nooshin Nemati +5 more
semanticscholar +1 more source
Histopathological image analysis is a pivotal area of medical research that leverages deep learning to derive quantitative insights from Hematoxylin and Eosin (H\&E) stained images.
Nooshin Nemati +5 more
semanticscholar +1 more source
Mask-Driven Mitosis Detection In Histopathology Images
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Mitotic detection and counting are the primary diagnostic factors used for cancer detection and grading. In this paper, we introduce a method of automatically obtaining masks for the cells and using the generated masks for mitotic detection. In the first stage of processing, we use the Mask R-CNN network to obtain the masks for the cells and also ...
Veena Dodballapur +5 more
openaire +1 more source
Journal of Innovative Image Processing
The primary limitation of CNN-based methods for mitosis detection in breast histopathological image classification is their inability to effectively extract features from potential regions of interest.
K. M., S. S.
semanticscholar +1 more source
The primary limitation of CNN-based methods for mitosis detection in breast histopathological image classification is their inability to effectively extract features from potential regions of interest.
K. M., S. S.
semanticscholar +1 more source
Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading
International journal of imaging systems and technology (Print), 2022Cancer grade is an indicator of the aggressiveness of cancer. It is used for prognosis and treatment decisions. Conventionally cancer grading is performed manually by experienced pathologists via microscopic examination of pathology slides.
Tojo Mathew +3 more
semanticscholar +1 more source
An Effective Multi-Patch Deep Learning Method for Mitosis Detection in Breast Histopathology
Journal of Trends in Computer Science and Smart TechnologyWithin the realm of computational pathology, the detection of mitotic cells poses a formidable challenge. Many existing approaches rely on hand-crafted features, which often result in poor generalization, as their performance degrades across different ...
K. M., S. S.
semanticscholar +1 more source
Automated mitosis detection with deep regression networks
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016Mitosis counting is one of the strongest prognostic markers for invasive breast cancer diagnosis. Clinical visual examination on histology slides by pathologists is tedious, error-prone and time-consuming. Furthermore, with the advent of whole slide imaging for high-throughput digitization, a large quantity of histology images need to be analyzed ...
Hao Chen, Xi Wang, Pheng Ann Heng
openaire +1 more source
Deep Fully Convolutional Networks for Mitosis Detection
Proceedings of the 2019 4th International Conference on Robotics, Control and Automation, 2019Image recognition plays a vital role in the medical image analysis field, which depends on different medical image analysis algorithms with input data, features, parameters, and type of learning. Three crucial morphological features on Hematoxylin and Eosin 1991 (HE our model is ResNet18 pre-trained to classify with localized based on the Tensorflow ...
Mohammed Abdulkareem +3 more
openaire +1 more source
Mitosis detection in breast cancer using dual-attention noise-free framework
Conference on Image, Signal Processing, and Pattern RecognitionMitosis counting is an important diagnostic criterion for breast cancer grading and prognosis. This paper proposes a novel mitosis detection method, the Dual-Attention Noise-Free Mitosis Detection Classification Framework (DAN-Mit), aimed at addressing ...
Kang Yu +4 more
semanticscholar +1 more source