F
Florian Ludwig
Publications - 4
Citations - 397
Florian Ludwig is an academic researcher. The author has contributed to research in topics: Breast cancer & Convolutional neural network. The author has an hindex of 4, co-authored 4 publications receiving 256 citations.
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Journal ArticleDOI
BACH: Grand challenge on breast cancer histology images.
Guilherme Aresta,Teresa Araújo,Scotty Kwok,Sai Saketh Chennamsetty,Mohammed Safwan,Varghese Alex,Bahram Marami,Marcel Prastawa,Monica Chan,Michael J. Donovan,Gerardo Fernandez,Jack Zeineh,Matthias Kohl,Christoph Walz,Florian Ludwig,Stefan Braunewell,Maximilian Baust,Quoc Dang Vu,Minh Nguyen Nhat To,Eal Kim,Jin Tae Kwak,Sameh Galal,Veronica Sanchez-Freire,Nadia Brancati,Maria Frucci,Daniel Riccio,Yaqi Wang,Lingling Sun,Kaiqiang Ma,Jiannan Fang,Ismael Kone,Lahsen Boulmane,Aurélio Campilho,Catarina Eloy,António Polónia,Paulo Aguiar +35 more
TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.
Book ChapterDOI
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
TL;DR: In this article, the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer was investigated.
Posted Content
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
TL;DR: This article investigates the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer.
Posted Content
Understanding Regularization to Visualize Convolutional Neural Networks.
TL;DR: The experiments performed on feature inversion and activation maximization demonstrate the benefit of a unified approach to regularization, such as sharper reconstructions via the proposed Sobolev filters and a better control over reconstructed scales.