Zobrazeno 1 - 10
of 509
pro vyhledávání: '"Reischl, Markus"'
Autor:
Bruch, Roman, Vitacolonna, Mario, Nürnberg, Elina, Sauer, Simeon, Rudolf, Rüdiger, Reischl, Markus
Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell dataset
Externí odkaz:
http://arxiv.org/abs/2408.16471
The assessment of bias within Large Language Models (LLMs) has emerged as a critical concern in the contemporary discourse surrounding Artificial Intelligence (AI) in the context of their potential impact on societal dynamics. Recognizing and conside
Externí odkaz:
http://arxiv.org/abs/2405.13041
Autor:
Sitcheu, Angelo Yamachui, Friederich, Nils, Baeuerle, Simon, Neumann, Oliver, Reischl, Markus, Mikut, Ralf
Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps). Nevertheless,
Externí odkaz:
http://arxiv.org/abs/2309.15521
Autor:
Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Weigert, Martin, Schmidt, Uwe, Zhang, Wenhua, Zhang, Jun, Yang, Sen, Xiang, Jinxi, Wang, Xiyue, Rumberger, Josef Lorenz, Baumann, Elias, Hirsch, Peter, Liu, Lihao, Hong, Chenyang, Aviles-Rivero, Angelica I., Jain, Ayushi, Ahn, Heeyoung, Hong, Yiyu, Azzuni, Hussam, Xu, Min, Yaqub, Mohammad, Blache, Marie-Claire, Piégu, Benoît, Vernay, Bertrand, Scherr, Tim, Böhland, Moritz, Löffler, Katharina, Li, Jiachen, Ying, Weiqin, Wang, Chixin, Kainmueller, Dagmar, Schönlieb, Carola-Bibiane, Liu, Shuolin, Talsania, Dhairya, Meda, Yughender, Mishra, Prakash, Ridzuan, Muhammad, Neumann, Oliver, Schilling, Marcel P., Reischl, Markus, Mikut, Ralf, Huang, Banban, Chien, Hsiang-Chin, Wang, Ching-Ping, Lee, Chia-Yen, Lin, Hong-Kun, Liu, Zaiyi, Pan, Xipeng, Han, Chu, Cheng, Jijun, Dawood, Muhammad, Deshpande, Srijay, Bashir, Raja Muhammad Saad, Shephard, Adam, Costa, Pedro, Nunes, João D., Campilho, Aurélio, Cardoso, Jaime S., S, Hrishikesh P, Puthussery, Densen, G, Devika R, C V, Jiji, Zhang, Ye, Fang, Zijie, Lin, Zhifan, Zhang, Yongbing, Lin, Chunhui, Zhang, Liukun, Mao, Lijian, Wu, Min, Vo, Vi Thi-Tuong, Kim, Soo-Hyung, Lee, Taebum, Kondo, Satoshi, Kasai, Satoshi, Dumbhare, Pranay, Phuse, Vedant, Dubey, Yash, Jamthikar, Ankush, Vuong, Trinh Thi Le, Kwak, Jin Tae, Ziaei, Dorsa, Jung, Hyun, Miao, Tianyi, Snead, David, Raza, Shan E Ahmed, Minhas, Fayyaz, Rajpoot, Nasir M.
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest
Externí odkaz:
http://arxiv.org/abs/2303.06274
Publikováno v:
Schulte, H. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. KIT Scientific Publishing, 2022
Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often instal
Externí odkaz:
http://arxiv.org/abs/2211.14417
To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for model validation. This dataset contains artificial spectra designed to r
Externí odkaz:
http://arxiv.org/abs/2206.06031
Autor:
Böhland, Moritz, Neumann, Oliver, Schilling, Marcel P., Reischl, Markus, Mikut, Ralf, Löffler, Katharina, Scherr, Tim
Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022) supports the development and comparability of segm
Externí odkaz:
http://arxiv.org/abs/2202.13960
Autor:
Schilling, Marcel P., Rettenberger, Luca, Münke, Friedrich, Cui, Haijun, Popova, Anna A., Levkin, Pavel A., Mikut, Ralf, Reischl, Markus
Publikováno v:
Proceedings - 31. Workshop Computational Intelligence, 2021
Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision app
Externí odkaz:
http://arxiv.org/abs/2111.13970
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 335-338 (2023)
U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. H
Externí odkaz:
https://doaj.org/article/1c95e857aaa246908336118ccedc7ddc
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 190-193 (2023)
We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a light
Externí odkaz:
https://doaj.org/article/1e4492dc19a54f64b47bd5e5d9879a66