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Histological Techniques

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Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels.

IEEE journal of biomedical and health informatics
Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucial step in the early diagnosis of liver cancer and is routinely performed manually. However, this pro...

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms.

IEEE journal of biomedical and health informatics
Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated re...

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images.

Journal of biomedical optics
We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in...

Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework.

IEEE transactions on medical imaging
Segmenting gland instances in histology images is highly challenging as it requires not only detecting glands from a complex background but also separating each individual gland instance with accurate boundary detection. However, due to the boundary ...

Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images.

IEEE transactions on medical imaging
Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224×...

[Deep Learning in Pathology: Applications and Challenges in Ophthalmic Pathology].

Klinische Monatsblatter fur Augenheilkunde
INTRODUCTION: Deep learning has received increasing attention in recent years and is used in many different areas. Since image analysis is a strength of deep learning, it would be obvious to use it for histopathological questions too. Our goal is to ...

Genetic algorithm search for the worst-case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks.

Magnetic resonance in medicine
PURPOSE: This paper presents a method to search for the worst-case configuration leading to the highest RF exposure for a multiconfiguration implantable fixation system under MRI.

Cellular community detection for tissue phenotyping in colorectal cancer histology images.

Medical image analysis
Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironme...

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...

Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology.

IEEE transactions on visualization and computer graphics
Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and patholog...