AIMC Topic: Cell Nucleus
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Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Nov 18, 2015
Robust and accurate nuclei localization in microscopy image can provide crucial clues for accurate computer-aid diagnosis. In this paper, we propose a convolutional neural network (CNN) based hough voting method to localize nucleus centroids with hea...
An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.
Scientific reports
Oct 9, 2015
This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster sca...
An Automatic Learning-Based Framework for Robust Nucleus Segmentation.
IEEE transactions on medical imaging
Sep 23, 2015
Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus...
Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis.
IEEE journal of biomedical and health informatics
Jul 29, 2015
In the analysis of histopathological images, both holistic (e.g., architecture features) and local appearance features demonstrate excellent performance, while their accuracy may vary dramatically when providing different inputs. This motivates us to...
Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 2, 2014
This study concerns a novel symbolic cognitive vision framework emerged from the Cognitive Microscopy (MICO(1)) initiative. MICO aims at supporting the evolution towards digital pathology, by studying cognitive clinical-compliant protocols involving ...
Image Enhancement
Software
Mitosis
Algorithms
Histological Techniques
Semantics
Neoplasm Grading
Female
Humans
Pattern Recognition, Automated
Reproducibility of Results
Signal Processing, Computer-Assisted
Sensitivity and Specificity
Machine Learning
Microscopy
Cell Nucleus
Breast Neoplasms
Image Interpretation, Computer-Assisted
User-Computer Interface
Enhancing nuclei segmentation in breast histopathology images using U-Net with backbone architectures.
Computers in biology and medicine
Jul 1, 2025
Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for accurate and timely diagnostic methods. Precise segmentation of nuclei in breast histopathology images is crucial for effective diagnosis and prognosi...
S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning.
Computers in biology and medicine
Jun 1, 2025
Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakl...
Weakly supervised nuclei segmentation based on pseudo label correction and uncertainty denoising.
Artificial intelligence in medicine
Jun 1, 2025
Nuclei segmentation plays a vital role in computer-aided histopathology image analysis. Numerous fully supervised learning approaches exhibit amazing performance relying on pathological image with precisely annotations. Whereas, it is difficult and t...
Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images.
Scientific reports
May 26, 2025
The detection, segmentation, and differentiation of benign and malignant nuclei from the histopathology images is a challenging task for the early diagnosis of breast cancer. Misinterpretation of True Negative (TN) and False Positive (FP) can generat...
Does artificial intelligence redefine nuclear-to-cytoplasmic ratio threshold for diagnosing high-grade urothelial carcinoma?
Cancer cytopathology
May 1, 2025
BACKGROUND: The Paris System (TPS) introduced standardized nuclear-to-cytoplasmic (N/C) ratio thresholds for urine cytology to improve high-grade urothelial carcinoma (HGUC) detection, but these criteria remain subjective. This study used AIxURO, an ...
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