AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy

no code implementations • 5 Oct 2023 • Nils Friederich, Angelo Yamachui Sitcheu, Oliver Neumann, Süheyla Eroğlu-Kayıkçı, Roshan Prizak, Lennart Hilbert, Ralf Mikut

In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor.

Active Learning Semantic Segmentation

MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis

1 code implementation • 27 Sep 2023 • Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle, Oliver Neumann, Markus Reischl, Ralf Mikut

The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps).

Transformer Training Strategies for Forecasting Multiple Load Time Series

1 code implementation • 19 Jun 2023 • Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer

We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients.

Load Forecasting Time Series +1

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

1 code implementation • 11 Mar 2023 • Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information

no code implementations • 6 Feb 2023 • Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer

Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly.

Time Series Time Series Analysis

EasyMLServe: Easy Deployment of REST Machine Learning Services

1 code implementation • 26 Nov 2022 • Oliver Neumann, Marcel Schilling, Markus Reischl, Ralf Mikut

We contribute an EasyMLServe software framework to deploy machine learning services in the cloud using REST interfaces and generic local or web-based GUIs.

Instance Segmentation Semantic Segmentation +2

ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network

no code implementations • 25 Feb 2022 • Moritz Böhland, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Katharina Löffler, Tim Scherr

Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making.

Classification Decision Making +3

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks

1 code implementation • 17 Nov 2021 • Oliver Neumann, Nicole Ludwig, Marian Turowski, Benedikt Heidrich, Veit Hagenmeyer, Ralf Mikut

In the present paper, we analyze the impact of data representations on the performance of Deep Neural Networks using energy time series forecasting.

Time Series Time Series Forecasting

On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned

no code implementations • bioRxiv 2021 • Tim Scherr, Katharina Löffler, Oliver Neumann, Ralf Mikut

The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task.

Cell Tracking Data Augmentation +1

pyWATTS: Python Workflow Automation Tool for Time Series

1 code implementation • 18 Jun 2021 • Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems.

Time Series Time Series Analysis