IroLaina

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Iro Laina

Contact

Iro Laina

Iro Laina

  • Email: iro.laina[at]tum.de

  • Address:

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München

  • Room:

  • Phone:

News


Teaching


Publications

2020
H. Dhamo, A. Farshad, I. Laina, N. Navab, G. D. Hager, F. Tombari, C. Rupprecht
Semantic Image Manipulation Using Scene Graphs
IEEE Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 2020
The first two authors contributed equally.
(bib)
2019
I. Laina, N. Navab, C. Rupprecht
Towards Unsupervised Image Captioning with Shared Multimodal Embeddings
International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019. (bib)
H. Dhamo, K. Tateno, I. Laina, N. Navab, F. Tombari
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Pattern Recognition Letters, Vol. 125, 2019 (bib)
2018
C. Rupprecht, I. Laina, N. Navab, G. D. Hager, F. Tombari
Guide Me: Interacting with Deep Networks
Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 2018.
Spotlight. The first two authors contribute equally to this paper.
(bib)
G. Ghazaei, I. Laina, C. Rupprecht, F. Tombari, N. Navab, K. Nazarpour
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
Asian Conference on Computer Vision and Pattern Recognition (ACCV), Perth, Australia, December 2018. (bib)
2017
C. Rupprecht, I. Laina, R. DiPietro, M. Baust, F. Tombari, N. Navab, G. D. Hager
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
International Conference on Computer Vision (ICCV 2017), Venice, Italy, October 2017 (bib)
I. Laina, N. Rieke, C. Rupprecht, J. Page Vizcaino, A. Eslami, F. Tombari, N. Navab
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Quebec, Canada, September 2017.
A pre-print version is available online at arXiv.
The first two authors contribute equally to this paper.
(bib)
K. Tateno, F. Tombari, I. Laina, N. Navab
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, June, 2017.
The first two authors contribute equally to this paper.
(bib)
2016
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, N. Navab
Deeper Depth Prediction with Fully Convolutional Residual Networks
International Conference on 3DVision (3DV), Stanford University, California, USA, October 2016.
Oral presentation. The first two authors contribute equally to this paper.
(bib)

Student Projects

Running
I will be not taking up on any more student projects.

Finished
Master ThesisExplained Predictions for Neural Networks
(Christian Rupprecht, Iro Laina, Federico Tombari, Prof. Nassir Navab)
DA/MA/BAWeakly supervised semantic segmentation via multiple hypothesis prediction
(Iro Laina, Christian Rupprecht, Federico Tombari, Nassir Navab)
Master Thesis3D Bounding Box Prediction from RGB and LiDAR Data Using 2D Proposals
(Iro Laina, Federico Tombari, Nassir Navab)
DA/MA/BADeep Learning for Depth Estimation from Single Images
(Iro Laina, Dr. Federico Tombari, Prof. Nassir Navab)
DA/MA/BADepth Prediction from Structured Light using Fully Convolutional Neural Networks
(Iro Laina, Keisuke Tateno, Federico Tombari, Prof. Nassir Navab)
DA/MA/BAAdversarial Multiple Hypothesis Prediction
(Christian Rupprecht, Iro Laina, Federico Tombari, Prof. Nassir Navab)
Master ThesisEfficient Object Detection using Fully Convolutional Neural Networks
(Iro Laina, Federico Tombari, Prof. Nassir Navab)
Master ThesisDeep Intrinsic Image Decomposition
(Christian Rupprecht, Iro Laina, Federico Tombari, Prof. Nassir Navab)


Research Projects

Depth Estimation from a single RGB Image

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Kooperationsprojekt SFB 824 (3. Förderperiode) & BFS

The goal of the BFS Project: Immunoprofiling using Neural Networks (IPN2) is to develop a method based on recent advances in Deep Learning to allow characterization of a patient's tumor as ″hot″ or ″cold″ depending on the identified Immunoprofile. Recent research has shown that many tumors are infiltrated by immuno-competent cells and that the amount, type and location of the infiltrated lymph nodes in primary tumors provide valuable prognostic information. In contrast to a ″cold tumor″, a ″hot tumor″ is characterized by an active immune system which the tumor has identified as threat. This identification provides the basis for selecting the therapy best suitable for the individual patient.

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Title: M.Sc.
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Firstname: Iro
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Groups: Computer Vision
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