Daniel Otero Baguer

About Daniel Otero Baguer

Daniel Otero Baguer, With an exceptional h-index of 8 and a recent h-index of 8 (since 2020), a distinguished researcher at Universität Bremen, specializes in the field of Neural Networks, Deep Learning, Inverse Problems, Applied Mathematics, Digital Pathology.

His recent articles reflect a diverse array of research interests and contributions to the field:

Smooth Deep Saliency

How GAN Generators can Invert Networks in Real-Time

Deep learning based histological classification of adnex tumors

Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms: Applying an artificial intelligence …

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma

Deep learning detection of melanoma metastases in lymph nodes

Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time

The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

Daniel Otero Baguer Information

University

Position

PostDoc

Citations(all)

432

Citations(since 2020)

431

Cited By

61

hIndex(all)

8

hIndex(since 2020)

8

i10Index(all)

6

i10Index(since 2020)

6

Email

University Profile Page

Google Scholar

Daniel Otero Baguer Skills & Research Interests

Neural Networks

Deep Learning

Inverse Problems

Applied Mathematics

Digital Pathology

Top articles of Daniel Otero Baguer

Smooth Deep Saliency

arXiv preprint arXiv:2404.02282

2024/4/2

Maximilian Schmidt
Maximilian Schmidt

H-Index: 2

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

How GAN Generators can Invert Networks in Real-Time

2024/2/27

Maximilian Schmidt
Maximilian Schmidt

H-Index: 2

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Deep learning based histological classification of adnex tumors

European Journal of Cancer

2024/1/1

Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms: Applying an artificial intelligence …

JDDG: Journal der Deutschen Dermatologischen Gesellschaft

2023/11

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma

JDDG: Journal der Deutschen Dermatologischen Gesellschaft

2023/11

Deep learning detection of melanoma metastases in lymph nodes

European Journal of Cancer

2023/7/1

Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time

arXiv preprint arXiv:2302.02181

2023/2/4

The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

medRxiv

2023

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

Cancers

2022/12/14

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Mark Kriegsmann
Mark Kriegsmann

H-Index: 24

Evaluation of a deep learning approach to differentiate Bowen’s disease and seborrheic keratosis

Cancers

2022/7/20

Staincut: stain normalization with contrastive learning

Journal of Imaging

2022/7

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Peter Maass
Peter Maass

H-Index: 26

LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction

Scientific Data

2021/4/16

Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of basal cell carcinoma

Journal of imaging

2021/4/13

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Peter Maass
Peter Maass

H-Index: 26

Inverse problems in designing new structural materials

2021

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Peter Maass
Peter Maass

H-Index: 26

A Benchmark for Deep Learning Reconstruction Methods for Low-Dose Computed Tomography

2020/4

Maximilian Schmidt
Maximilian Schmidt

H-Index: 2

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Regularization by architecture: A deep prior approach for inverse problems

Journal of Mathematical Imaging and Vision

2020/4

A deep prior approach to magnetic particle imaging

2020

Computed tomography reconstruction using deep image prior and learned reconstruction methods

Inverse Problems

2020/9/2

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

Maximilian Schmidt
Maximilian Schmidt

H-Index: 2

Neural Networks for solving Inverse Problems: Applications in Materials Science and Medical Imaging

2020/7/9

Daniel Otero Baguer
Daniel Otero Baguer

H-Index: 3

See List of Professors in Daniel Otero Baguer University(Universität Bremen)