activity
20162022
most citedRobust Medical Instrument Segmentation Challenge 2019

33 citations · 53 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 3 cited

Labeling instructions matter in biomedical image analysis

Tim Rädsch, Annika Reinke, Vivienn Weru +7

Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labeling instructions are key. Despite their importance, their op…

cs.CV2021★ 3 cited

How can we learn (more) from challenges? A statistical approach to driving future algorithm development

Tobias Roß, Pierangela Bruno, Annika Reinke +12

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step fo…

cs.CV2020

Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room

Lena Maier-Hein, Martin Wagner, Tobias Ross +30

Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracki…

cs.CV2020★ 33 cited

Robust Medical Instrument Segmentation Challenge 2019

Tobias Ross, Annika Reinke, Peter M. Full +47

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tr…

cs.LG2019

Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

Micha Pfeiffer, Isabel Funke, Maria R. Robu +12

In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, c…

cs.CV2017

Exploiting the potential of unlabeled endoscopic video data with self-supervised learning

Tobias Ross, David Zimmerer, Anant Vemuri +12

Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to t…

cs.CV2016★ 14 cited

Clickstream analysis for crowd-based object segmentation with confidence

Eric Heim, Alexander Seitel, Jonas Andrulis +4

With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of…