AIMC Topic: Histological Techniques
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Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.
PLoS computational biology
Mar 27, 2019
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing resu...
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.
Scientific reports
Mar 4, 2019
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteri...
Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex.
Journal of chemical neuroanatomy
Mar 2, 2019
Collection of unbiased stereology data currently relies on relatively simple, low throughput technology developed in the mid-1990s. In an effort to improve the accuracy and efficiency of these integrated hardware-software-digital microscopy systems, ...
MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.
Medical image analysis
Dec 20, 2018
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectiv...
Micro-Net: A unified model for segmentation of various objects in microscopy images.
Medical image analysis
Dec 15, 2018
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy i...
Transfer learning for classification of cardiovascular tissues in histological images.
Computer methods and programs in biomedicine
Aug 16, 2018
BACKGROUND AND OBJECTIVE: Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical prac...
Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks.
IEEE journal of biomedical and health informatics
Jul 3, 2018
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for var...
Weakly Supervised Biomedical Image Segmentation by Reiterative Learning.
IEEE journal of biomedical and health informatics
Jun 25, 2018
Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as ov...
Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.
IEEE journal of biomedical and health informatics
Apr 16, 2018
Compact binary representations of histopa-thology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality ...
Predicting cancer outcomes from histology and genomics using convolutional networks.
Proceedings of the National Academy of Sciences of the United States of America
Mar 12, 2018
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digit...
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