Advances in experimental medicine and biology
Jan 1, 2020
Histology, the branch of anatomy also known as microscopic anatomy, is the study of the structure and function of the body's tissues. To gain an understanding of the tissues of the body is to learn the foundational underpinnings of anatomy and achiev...
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...
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables th...
BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis...
Studies in health technology and informatics
Aug 21, 2019
We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding and text classification). We compared word embeddings (Fasttext) and language models (ELMo), learned...
Brain and nerve = Shinkei kenkyu no shinpo
Jul 1, 2019
Rapid improvements in computing power are advancing machine learning technology using neural networks, revolutionizing the field of image analysis and allowing for automated diagnosis of pathological images. In addition, the recent development of tis...
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, ...
AIMS: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many ...
IEEE journal of biomedical and health informatics
May 1, 2019
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...
IEEE journal of biomedical and health informatics
May 1, 2019
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...