K
Kaisa Liimatainen
Researcher at University of Tampere
Publications - 10
Citations - 2221
Kaisa Liimatainen is an academic researcher from University of Tampere. The author has contributed to research in topics: Digital pathology & Deep learning. The author has an hindex of 4, co-authored 10 publications receiving 1434 citations. Previous affiliations of Kaisa Liimatainen include HTW Berlin - University of Applied Sciences & Tampere University of Technology.
Papers
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Journal ArticleDOI
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Babak Ehteshami Bejnordi,Mitko Veta,Paul J. van Diest,Bram van Ginneken,Nico Karssemeijer,Geert Litjens,Jeroen van der Laak,Meyke Hermsen,Quirine F. Manson,Maschenka Balkenhol,Oscar Geessink,N. Stathonikos,Marcory C. R. F. van Dijk,Peter Bult,Francisco Beca,Andrew H. Beck,Dayong Wang,Aditya Khosla,Rishab Gargeya,Humayun Irshad,Aoxiao Zhong,Qi Dou,Qi Dou,Quanzheng Li,Hao Chen,Huangjing Lin,Pheng-Ann Heng,Christian Haß,Elia Bruni,Quincy Wong,Ugur Halici,Mustafa Umit Oner,Rengul Cetin-Atalay,Matt Berseth,Vitali Khvatkov,Alexei Vylegzhanin,Oren Kraus,Muhammad Shaban,Nasir M. Rajpoot,Nasir M. Rajpoot,Ruqayya Awan,Korsuk Sirinukunwattana,Talha Qaiser,Yee-Wah Tsang,David Tellez,Jonas Annuscheit,Peter Hufnagl,Mira Valkonen,Kimmo Kartasalo,Kimmo Kartasalo,Leena Latonen,Pekka Ruusuvuori,Pekka Ruusuvuori,Kaisa Liimatainen,Shadi Albarqouni,Bharti Mungal,Ami George,Stefanie Demirci,Nassir Navab,Seiryo Watanabe,Shigeto Seno,Yoichi Takenaka,Hideo Matsuda,Hady Ahmady Phoulady,Vassili Kovalev,A. Kalinovsky,Vitali Liauchuk,Gloria Bueno,M. Milagro Fernández-Carrobles,Ismael Serrano,Oscar Deniz,Daniel Racoceanu,Daniel Racoceanu,Rui Venâncio +73 more
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Journal ArticleDOI
Metastasis detection from whole slide images using local features and random forests.
Mira Valkonen,Kimmo Kartasalo,Kimmo Kartasalo,Kaisa Liimatainen,Kaisa Liimatainen,Matti Nykter,Matti Nykter,Leena Latonen,Pekka Ruusuvuori,Pekka Ruusuvuori +9 more
TL;DR: A machine learning approach for detection of cancerous tissue from scanned whole slide images based on feature engineering and supervised learning with a random forest model that detects metastatic areas with high accuracy and generalizes well for images from more than one laboratory.
Journal ArticleDOI
Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns.
TL;DR: In this paper, the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments was studied, and the fully convolutional network outperformed the CNN in the classification of images with multiple simultaneous protein localizations.
Proceedings ArticleDOI
Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology
TL;DR: The model was able to accurately discriminate cancerous tissue from normal tissue, resulting in blockwise AUC=0.97, where the total number of analyzed tissue blocks was approximately 8.3 million that constitute the test set of 75 whole slide images.
Proceedings ArticleDOI
Supervised method for cell counting from bright field focus stacks
TL;DR: It is concluded that using several focal planes provides valuable intensity information for cell counting from bright field microscopy by a novel method based on the use of supervised learning and out-of-focus appearance of cells.