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Fred A. Hamprecht

Fred A. Hamprecht

D-Index & Metrics

Computer Science

D-Index
57
Citations
16034
World Ranking
3779
National Ranking
168

Overview

Fred A. Hamprecht is affiliated with Heidelberg University in Germany, contributing extensively to research at the intersection of biochemistry, genetics, molecular biology, and computer science. Their work spans multiple subfields, including molecular biology, computer vision and pattern recognition, plant science, biophysics, and artificial intelligence.

Hamprecht's research primarily focuses on several core topics:

  • Cell Image Analysis Techniques
  • Single-cell and spatial transcriptomics
  • Plant Molecular Biology Research
  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications
  • Smart Agriculture and AI
  • Plant Reproductive Biology

The scientist has published a significant number of papers in various venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Lecture Notes in Computer Science
  • eLife

Selected recent publications by Hamprecht include:

  • "Accurate and versatile 3D segmentation of plant tissues at cellular resolution," 2020, eLife
  • "A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule," 2021, eLife
  • "Seipin forms a flexible cage at lipid droplet formation sites," 2022, Nature Structural & Molecular Biology
  • "The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning," 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Temporal control of the integrated stress response by a stochastic molecular switch," 2022, Science Advances

Collaboration is a notable aspect of Hamprecht's research activity. Frequent co-authors who have contributed to their work include:

  • Lorenzo Cerrone
  • Anna Kreshuk
  • Athul Vijayan
  • Kay Schneitz
  • Adrian Wolny

Best Publications

  • ilastik: interactive machine learning for (bio)image analysis.

    Stuart Berg;Dominik Kutra;Thorben Kroeger;Christoph N Straehle

  • A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

    Bjoern H Menze;B Michael Kelm;Ralf Masuch;Uwe Himmelreich

  • Ilastik: Interactive learning and segmentation toolkit

    Christoph Sommer;Christoph Straehle;Ullrich Kothe;Fred A. Hamprecht

  • An objective comparison of cell-tracking algorithms

    Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger

  • On the Spectral Bias of Neural Networks

    Nasim Rahaman;Aristide Baratin;Devansh Arpit;Felix Draxler

  • Learning Steerable Filters for Rotation Equivariant CNNs

    Maurice Weiler;Fred A. Hamprecht;Martin Storath

  • Accurate and versatile 3D segmentation of plant tissues at cellular resolution

    Adrian Wolny;Lorenzo Cerrone;Athul Vijayan;Rachele Tofanelli

  • A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

    Jorg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnorr

  • Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.

    Hugo Kubinyi;Fred A. Hamprecht;Thomas Mietzner

  • On oblique random forests

    Bjoern H. Menze;B. Michael Kelm;Daniel N. Splitthoff;Ullrich Koethe

  • Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics

    Thomas Koenig;Bjoern H. Menze;Marc Kirchner;Flavio Monigatti

  • Multi-modal Brain Tumor Segmentation using Deep Convolutional Neural Networks

    G. Urban;M. Bendszus;F. A. Hamprecht;J. Kleesiek

  • A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

    Jörg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnörr

  • Learning to count with regression forest and structured labels

    Luca Fiaschi;Ullrich Koethe;Rahul Nair;Fred A. Hamprecht

  • Essentially No Barriers in Neural Network Energy Landscape

    Felix Draxler;Kambis Veschgini;Manfred Salmhofer;Fred A. Hamprecht

  • Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy.

    Martin Pfannmöller;Harald Flügge;Gerd Benner;Irene Wacker

  • Imagining the future of bioimage analysis

    Erik Meijering;Anne E Carpenter;Hanchuan Peng;Fred A Hamprecht

  • Multicut brings automated neurite segmentation closer to human performance

    Thorsten Beier;Constantin Pape;Nasim Rahaman;Timo Prange

  • Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images.

    Anna Kreshuk;Christoph N. Straehle;Christoph Sommer;Ullrich Koethe

  • Concise Representation of Mass Spectrometry Images by Probabilistic Latent Semantic Analysis

    Michael Hanselmann;Marc Kirchner;Bernhard Y. Renard;Erika R. Amstalden

  • Author response: Accurate and versatile 3D segmentation of plant tissues at cellular resolution

    Adrian Wolny;Adrian Wolny;Lorenzo Cerrone;Athul Vijayan;Rachele Tofanelli

  • A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

    Bjoern Holger Menze;Bernd Michael Kelm;Ralf Masuch;Uwe Himmerlreich

Frequent Co-Authors

Bjoern H. Menze
Bjoern H. Menze University of Zurich
Bernhard Y. Renard
Bernhard Y. Renard Hasso Plattner Institute
Hanno Steen
Hanno Steen Boston Children's Hospital
Graham Knott
Graham Knott École Polytechnique Fédérale de Lausanne
Bernd Jähne
Bernd Jähne Heidelberg University
Christoph Schnörr
Christoph Schnörr Heidelberg University
Steeve Boulant
Steeve Boulant University of Florida
Albert Cardona
Albert Cardona University of Cambridge
Boaz Nadler
Boaz Nadler Weizmann Institute of Science
Erik Agrell
Erik Agrell Chalmers University of Technology

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