◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

M. Hägele

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV1
  • cs.LG1
  • eess.IV1
  • math.PR1

identity via Semantic Scholar / OpenAlex

activity
20182021
collaborators

4 papers

math.PR2021

Large deviations for a class of multivariate heavy-tailed risk processes used in insurance and finance

Miriam Hägele, Jaakko Lehtomaa

Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance port…

eess.IV2019

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Miriam Hägele, Philipp Seegerer, Sebastian Lapuschkin +5

Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better unde…

cs.LG2018

iNNvestigate neural networks!

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7

In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. The…

cs.CV2018

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Alexander Binder, Michael Bockmayr, Miriam Hägele +15

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.