activity
20192026
most citedMulti-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture

116 citations · 145 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation

Linus Britt, Maximilian Nielsen, Susan Klapproth +5

Objectives: Quantitative assessment of infarct hypodensity on non-contrast computed tomography (NCCT), including net water uptake (NWU), requires manual or semi-manual lesion delin…

cs.CV2023★ 6 cited

Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025

Aneeq Zia, Max Berniker, Rogerio Garcia Nespolo +153

Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms…

cs.CV2023★ 23 cited

Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per class

Maximilian Nielsen, Laura Wenderoth, Thilo Sentker +1

Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question…

cs.CV2019

Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data

Nils Gessert, Maximilian Nielsen, Mohsin Shaikh +2

In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified bas…

eess.IV2019★ 116 cited

Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture

Rüdiger Schmitz, Frederic Madesta, Maximilian Nielsen +3

Histopathologic diagnosis relies on simultaneous integration of information from a broad range of scales, ranging from nuclear aberrations () through…