Results 41 to 50 of about 345,506 (334)

Transfer Learning Approach for Classification of Histopathology Whole Slide Images

Sensors, 2021
The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis.
Shakil Ahmed, Asadullah Shaikh, Hani Alshahrani, Abdullah Alghamdi, Mesfer Alrizq, Junaid Baber, Maheen Bakhtyar   +6 more
doaj   +1 more source

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

, 2017
Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training,
Hassanpour, Saeed, Korbar, Bruno, Miraflor, Allen P., Nicka, Katherine M., Olofson, Andrea M., Suriawinata, Arief A., Suriawinata, Matthew A., Torresani, Lorenzo   +7 more
core   +2 more sources

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload. [PDF]

, 2020
Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and ...
Ayyagari, Devi, Bonham, Michael J., Chamarthi, Ramachandra Vikas, Cockerell, Clay J., Feeser, Theresa A., Ianni, Julianna D., Lee, Jason B., Motaparthi, Kiran, Olsen, Thomas G., Sankarapandian, Sivaramakrishnan, Soans, Rajath E., Stavish, Coleman C.   +11 more
core   +1 more source

Simultaneous automatic scoring and co-registration of hormone receptors in tumour areas in whole slide images of breast cancer tissue slides [PDF]

, 2016
Aims: Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers in order to predict the patient's response to targeted ...
Cree, Ian A., Epstein, D. B. A., Rajpoot, Nasir M., Snead, David, Trahearn, Nicholas, Tsang, Yee-Wah   +5 more
core   +1 more source

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Frontiers in Computer Science, 2021
Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases.
Niccolò Marini, Niccolò Marini, Sebastian Otálora, Sebastian Otálora, Damian Podareanu, Mart van Rijthoven, Jeroen van der Laak, Jeroen van der Laak, Francesco Ciompi, Henning Müller, Henning Müller, Manfredo Atzori, Manfredo Atzori   +12 more
doaj   +1 more source

SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images

, 2018
Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist.
Aubreville, Marc, Bertram, Christof, Klopfleisch, Robert, Maier, Andreas   +3 more
core   +1 more source

Whole slide images technology: preliminary experience in clinical microbiology

Microbiologia Medica, 2013
Today the “whole slide images” (virtual slides) is an important tool also in clinical microbiology. Virtual microscopy consists of digitizing a glass slide acquiring hundred of tiles of areas of interest at different resolution levels, and assembling ...
Enrico Magliano, Tshering Dorji, Giovanni Fellegara, Davide Guarneri, Vittorio Grazioli   +4 more
doaj   +1 more source

Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images [PDF]

IEEE Transactions on Biomedical Engineering, 2016
Image registration of whole slide histology images allows the fusion of fine-grained information-like different immunohistochemical stains-from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time.
Lotz, J., Olesch, J., Muller, B., Polzin, T., Galuschka, P., Lotz, J. M., Heldmann, S., Laue, H., Gonzalez-Vallinas, M., Warth, A., Lahrmann, B., Grabe, Niels, Sedlaczek, O., Breuhahn, K., Modersitzki, J.   +14 more
openaire   +4 more sources

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

, 2017
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area.
Balkenhol, Maschenka, Bejnordi, Babak Ehteshami, Bult, Peter, Hermsen, Meyke, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen, van Ginneken, Bram, Zuidhof, Guido   +8 more
core   +1 more source

Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer [PDF]

, 2006
Background Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides.
Sokol Petushi, Fernando U Garcia, Marian M Haber, Constantine Katsinis, Aydin Tozeren, RW Scarff, CW Elston, I Balslev, R Albert, LW Dalton, A Moore, CA Roberts, W Han, M Hoglund, TK Jennsen, L Liotta, L Latson, F Schnorrenberg, S Schupp, JW Bacus, A Hoque, WH Wolberg, S Petushi, O Otsu, M Sonka   +24 more
core   +2 more sources