Learning and Understanding in 3D Point Clouds (Video1)Video Length:
We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. In particular, we propose to couple the object detection with a coarse-to-fine segmentation, where each segment is subject to disjoint pose estimation. During matching, we apply a weighted Hough voting and an interpolated recovery of pose parameters. Finally, all the generated hypotheses are tested via an occlusion-aware ranking and sorted. We argue that such a combined pipeline simultaneously boosts the detection rate and reduces the complexity, while improving the accuracy of the resulting pose.
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.
3 September 2020, Garching/ Transla TUM /Online CAMP Pizza! September 2020 Edition We are very pleased to announce great speakers to CAMP Pizza on Sept. 3 2020. This time in the event series Prof. Daniel Rückert&, Ph.D (Imperial College London), Cristina Piazza&, Ph.D (Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL (USA), and Dr Georgios Kaissis, MHBA (Institute of Diagnostic and Interventional Radiology and the Institute for Artificial Intelligence and Data Science in Healthcare and Medicine at the Technical University of Munich) will join our event in addition to talks by Prof. Navab, Benjamin Busam, Sasan Martinfar, Alexander Lehner, and Sen Wang. The event will be held in two physical locations (Garching and Transla TUM) as well as virtual.
2 September 2020, Online Invited Talk by Dr. Tolga Birdal: A Geometric Take on Probabilistic Synchronization Tolga Birdal will present an unconventional sequel of works bringing fresh perspectives to the 2D/3D multiview alignment, correspondence estimation and optimization problems. In this peculiar talk, Tolga interrogates the mono-optimality in vision problems and argues for an interpolatant between optimization and statistical inference that characterize the space and geometry of solutions for a specific downstream task. Within the scope, the problem of particular interest is synchronization, operation to unison. Tolga will start off with the nuts and bolts on probability and inference in geometric domains such as the Riemannian tempered stochastic gradient MCMC methods. He will then apply different variations of this algorithm to various group valued non-Euclidean parameters (rotations, poses and permutations) belonging to the highly non-convex problem of synchronization within an undirected graph. Subsequently, he will leap into non-parametric methods and show how in the most general case, cyclic-consistency of distributions on maps can be understood as a sum of optimal transportations in the Lie group of the parameters. In the remainder of the talk, he will discuss how some of these algorithms can be used in contemporary deep neural networks as well as alternative twists to synchronize: multi-scan registration without registration. Findings relevant to this project are published in NeurIPS? 2018, CVPR 2019 & CVPR 2020 as well as the CVPR 2020 tutorial on synchronization: http://synchinvision.github.io/. The methods are particularly suited to improve SFM (structure from motion) and SLAM (simultaneous localization and mapping) pipelines that are essential for computer vision and robotics.
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.
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.
23 July 2020, Five Papers accepted at IROS 2020 , Three of them also at RAL We are happy to announce that three papers from our Chair are accepted by IEEE Robotics and Automation Letters and together with two more papers from out chair will be presented at the International Conference on Intelligent Robots and Systems (IROS 2020) in Las Vegas, USA, October 25 - 29, 2020.
3 July 2020, 4 papers accepted at ECCV 2020 We are happy to announce that 4 papers from our Chair were accepted and will be presented at the 2020 European Conference on Computer Vision (ECCV 2020), August 24 - 28, 2020.