Fully Immersive Simulation Environment (trivial phone call distraction) Video Length:
A trivial phone call scenario which distracts one resident surgeon compared to the other. This distraction resulted on higher cognitive workload and a decrease in technical accuracy during simulation surgery training.
15 January 2021, Zoom CAMP New Year's Party 2021 Happy 2021! To celebrate the arrival of the new year and connect with each other during these challenging times, the CAMP chair is hosting a virtual New Year's Party on Friday, January 15th. The event will feature educational talks about Transformers, career advice by selected Alumni and will conclude with a fun game night!
9 December 2020, Zoom CAMP Pizza! December 2020 Edition The last but not least CAMPPizza in 2020 will take place on December 9th from 10:00-15:00. It will be held purely virtual over Zoom. We are pleased to announce our main speakers, Dr. Federico Tombari (Google, Zürich), Dr. Pierre Jannin (Medical School of the University of Rennes, France) and Dr. Steven McDonagh (Huawei Technologies R&D, London) and welcome our new members and returners Tianyu Song, Heiko Maier, Yousef Yeganeh and Mahdi Hamad.
9 December 2020, YouTube EIT Health FastRAi Symposium We would like to invite you to the virtual FastRAi symposium, where CAMP, Deepc and M3i will present the methods and findings developed for the project 'Flexible and Adaptive Software-Framework for X-Ray-enabled AI for COVID-19 detection'.
22 October 2020, Deep Learning Solutions for Cancer Drug Development in Digital Pathology In the domain of oncology, pathology tasks such as patient diagnosis and cancer drug development, have been revolutionized since the growing availability and quality of high-resolution whole slide scanners. The transition from conventional glass slide microscopic assessment to digital pathology has been beneficial and promising for both pathologists and researchers due to the ability to collect, process and analyze much larger datasets with more stains (clinical markers) using more objective, accurate and consistent methods. Furthermore, with the recent advances in computational power, deep learning solutions have been developed allowing to explore previously unachievable predictive algorithms and to enhance the value and insights that can be generated from pathology tissue images. However, different challenges need to be taken into consideration in order to make the applications more efficient and to increase confidence and adoption in the very conservative field of human pathology. These challenges include, among others, application generalization over multiple stains, limitations in terms of tissue staining materials and procedures, size of histologic whole slide images (WSis) and intra and inter-pathologist variability. In this thesis, we explore new methodologies, techniques, and deep learning solutions to the aforementioned challenges in the context of two different applications: stain generalization and stain virtualization applied to digital images of Colorectal Carcinoma metastases in liver tissue from biopsy and surgical specimen. Slide annotation is a key step in pathology routines and in cancer biomarker research aiming to quantify pattern changes in microscopic WSis of tumor biopsies. The process of manual annotation can be tedious and subjective, especially in the context of drug development research where a correlative analysis of multiple biomarkers is required. In the first part of this dissertation, we elaborate on a supervised learning-based method that allows to segment different functional compartments in histology images of various stainings including Hematoxylin and Eosin (H&E) staining and multiple immunohistochemistry (IHC) stainings. We show the effect of the proposed solution on increasing the stain generalization performance and dealing with dataset variability by comparing to state-of-the-art methods and using different visualization techniques. In the second part, we propose stain virtualization solutions allowing to virtually generate an IHC staining from different input stainings using unsupervised learning methods. Stain virtualization presents many advantages to the pathology drug development workflow, such as reduction of lab workload, reduction in tissue and costs, and multiplexing of different biomarkers on the same tissue and with the same coordinate system. In this context, we additionally propose solutions to one of the main challenges of high-resolution style transfer in general and to WSI processing in particular, i.e. the tiling artifact caused by the necessity of tilewise processing. We describe different experiments and visualizations aiming to prove the hypothesis and we validate the application mathematically and histologically.
7 September 2020, IFL Lab Research Engineer Position The CAMP chair at the TUM is looking for a Lab Research Engineer to join the research team at Klinikum rechts der Isar in Munich Germany.
28 December 2020, IFL SAT.1 Bayern feature on CAMP’s projects against COVID SAT.1 Bayern features a reportage on the efforts of the CAMP Chair in collaboration with the Radiology of Klinikum Rechts der Isar to fight COVID-19 and equip physicians with solutions that could further be useful for future pandemics.
30 November 2020, Kyoto, Japan 2 papers accepted at ACCV 2020 We are happy to announce that 2 papers from our group were accepted and will be presented at the 2020 Asian Conference on Computer Vision (ACCV 2020), November 30 - December 04, 2020.
25 November 2020, Online, Japan 2 papers accepted at 3DV 2020 We are happy to announce that 2 papers from our group were accepted and will be presented at the International Conference on 3D Vision (3DV 2020), November 25 - 28, 2020.
9 November 2020, Recife/Porto De Galinhas, Brasil 3 Papers accepted at ISMAR 2020 We are happy to announce that several papers of our chair will be presented at the IEEE International Symposium on Mixed and Augmented Reality that will happen between November 09th and 13th 2020.
31 August 2020, Lima, Peru 15 papers accepted at MICCAI 2020 We are happy to announce that 15 papers of our chair will be presented at the 23rd International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI 2020) in Lima, Peru, October 4-8, 2020.