Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment

Autor: Pekka Ruusuvuori, Masi Valkonen, Kimmo Kartasalo, Mira Valkonen, Tapio Visakorpi, Matti Nykter, Leena Latonen

Publikováno v: Heliyon, Vol 8, Iss 1, Pp e08762- (2022)

Histological changes in tissue are of primary importance in pathological research and diagnosis. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue. Conventional histopathological

Externí odkaz: https://doaj.org/article/2242782a1d2044799139317878fa7540

The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility

Autor: Umair Khan, Sonja Koivukoski, Mira Valkonen, Leena Latonen, Pekka Ruusuvuori

Publikováno v: Patterns. 4:100725

Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4aac4f4213c29641b3f6b99a33ba442
https://doi.org/10.1016/j.patter.2023.100725

Generalized fixation invariant nuclei detection through domain adaptation based deep learning

Autor: Mira, Valkonen, Gunilla, Hognas, G Steven, Bova, Pekka, Ruusuvuori

Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::8ebe3944ce90e2289ff02e6b6c7cf9ef
https://trepo.tuni.fi/handle/10024/137032

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Autor: Ugur Halici, Rishab Gargeya, Quincy Wong, Hady Ahmady Phoulady, David Tellez, Bram van Ginneken, Andrew H. Beck, Nico Karssemeijer, Jeroen van der Laak, Nassir Navab, Jonas Annuscheit, Leena Latonen, Kaisa Liimatainen, Talha Qaiser, Dayong Wang, Quirine F. Manson, Aoxiao Zhong, Shigeto Seno, Yee-Wah Tsang, Rui Venâncio, Ismael Serrano, Daniel Racoceanu, N. Stathonikos, Muhammad Shaban, Stefanie Demirci, M. Milagro Fernández-Carrobles, Babak Ehteshami Bejnordi, Matt Berseth, Mustafa Umit Oner, Geert Litjens, Kimmo Kartasalo, Hideo Matsuda, Maschenka Balkenhol, Huangjing Lin, Elia Bruni, Hao Chen, Seiryo Watanabe, A. Kalinovsky, Marcory C. R. F. van Dijk, Ami George, Nasir M. Rajpoot, Francisco Beca, Quanzheng Li, Meyke Hermsen, Mira Valkonen, Oscar Deniz, Alexei Vylegzhanin, Vitali Liauchuk, Ruqayya Awan, Mitko Veta, Korsuk Sirinukunwattana, Gloria Bueno, Peter Hufnagl, Christian Haß, Vassili Kovalev, Vitali Khvatkov, Rengul Cetin-Atalay, Humayun Irshad, Oren Kraus, Qi Dou, Pekka Ruusuvuori, Aditya Khosla, Bharti Mungal, Pheng-Ann Heng, Oscar Geessink, Paul J. van Diest, Shadi Albarqouni, Peter Bult, Yoichi Takenaka

Publikováno v: JAMA Cardiology
JAMA Cardiology, American Medical Association 2017, 318, ⟨10.1001/jama.2017.14585⟩
Jama : Journal of the American Medical Association, 318, 2199-2210
JAMA Neurology, 318(22), 2199-2210. American Medical Association (AMA)
Jama : Journal of the American Medical Association, 318, 22, pp. 2199-2210
JAMA-The Journal of The American Medical Association, 318(22), 2199. American Medical Association

IMPORTANCE: Application of deep learning algorithms to whole-slide pathology imagescan potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5e2e1ebc60e746d57a0deffa5ee651b
https://hal.archives-ouvertes.fr/hal-03140979/document

Metastasis detection from whole slide images using local features and random forests

Autor: Mira, Valkonen, Kimmo, Kartasalo, Kaisa, Liimatainen, Matti, Nykter, Leena, Latonen, Pekka, Ruusuvuori

Publikováno v: Cytometry. Part A : the journal of the International Society for Analytical Cytology. 91(6)

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::5d19cfa687356ff512dd910560eef082
https://pubmed.ncbi.nlm.nih.gov/28426134