AIMC Topic: Histocytochemistry
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SetSVM: An Approach to Set Classification in Nuclei-Based Cancer Detection.
IEEE journal of biomedical and health informatics
Feb 8, 2018
Due to the importance of nuclear structure in cancer diagnosis, several predictive models have been described for diagnosing a wide variety of cancers based on nuclear morphology. In many computer-aided diagnosis (CAD) systems, cancer detection tasks...
Constrained Deep Weak Supervision for Histopathology Image Segmentation.
IEEE transactions on medical imaging
Jul 7, 2017
In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS);...
Metastasis detection from whole slide images using local features and random forests.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Apr 20, 2017
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...
Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.
Computers in biology and medicine
Apr 18, 2017
Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While...
Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.
IEEE journal of biomedical and health informatics
Apr 6, 2017
Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neu...
Gland Instance Segmentation Using Deep Multichannel Neural Networks.
IEEE transactions on bio-medical engineering
Mar 23, 2017
OBJECTIVE: A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also b...
Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.
Computers in biology and medicine
Mar 22, 2017
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) arch...
A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Feb 13, 2017
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distingu...
Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications.
IEEE transactions on pattern analysis and machine intelligence
Jan 23, 2017
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques ...
Biologically Relevant Heterogeneity: Metrics and Practical Insights.
SLAS discovery : advancing life sciences R & D
Jan 6, 2017
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medic...
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