Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce en...
Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maint...
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...
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...
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...
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...
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 ...
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...
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...
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...
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