Publications

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2020

Detection of Breast Cancer From Whole Slide Histopathological Images Using Deep Multiple Instance CNN. Das, K.; Conjeti, S.; Chatterjee, J.; and Sheet, D. IEEE Access, 8: 213502–213511. 2020.
 

FastSurfer - A fast and accurate deep learning-based neuroimaging pipeline. Henschel, L.; Conjeti, S.; Estrada, S.; Diers, K.; Fischl, B.; and Reuter, M. NeuroImage, 219: 117012. 2020.
 

Abstract: Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI. Estrada, S.; Lu, R.; Conjeti, S.; Orozco, X.; Panos, J.; Breteler, M. M. B.; and Reuter, M. In Tolxdorff, T.; Deserno, T. M.; Handels, H.; Maier, A. K.; Maier-Hein, K. H.; and Palm, C., editor(s), Bildverarbeitung für die Medizin 2020 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin, of Informatik Aktuell, pages 74, 2020. Springer
 

Abstract: FastSurfer. Henschel, L.; Conjeti, S.; Estrada, S.; Diers, K.; Fischl, B.; and Reuter, M. In Tolxdorff, T.; Deserno, T. M.; Handels, H.; Maier, A. K.; Maier-Hein, K. H.; and Palm, C., editor(s), Bildverarbeitung für die Medizin 2020 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin, of Informatik Aktuell, pages 208, 2020. Springer
 

2019

QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. Roy, A. G.; Conjeti, S.; Navab, N.; Wachinger, C.; and Initiative, A. D. N. NeuroImage, 186: 713–727. 2019.

  

Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control. Roy, A. G.; Conjeti, S.; Navab, N.; and Wachinger, C. NeuroImage, 195: 11–22. 2019.
 

Abstract: Adversarial Examples as Benchmark for Medical Imaging Neural Networks. Paschali, M.; Conjeti, S.; Navarro, F.; and Navab, N. In Handels, H.; Deserno, T. M.; Maier, A. K.; Maier-Hein, K. H.; Palm, C.; and Tolxdorff, T., editor(s), Bildverarbeitung für die Medizin 2019 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck, of Informatik Aktuell, pages 14, 2019. Springer Vieweg
 

Abstract: Leveraging Web Data for Skin Lesion Classification. Navarro, F.; Conjeti, S.; Tombari, F.; and Navab, N. In Handels, H.; Deserno, T. M.; Maier, A. K.; Maier-Hein, K. H.; Palm, C.; and Tolxdorff, T., editor(s), Bildverarbeitung für die Medizin 2019 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck, of Informatik Aktuell, pages 199, 2019. Springer Vieweg
 

Deep Learning for Volumetric Segmentation in Spatio-Temporal Data: Application to Segmentation of Prostate in DCE-MRI. Kang, J.; Samarasinghe, G.; Senanayake, U.; Conjeti, S.; and Sowmya, A. In 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italy, April 8-11, 2019, pages 61–65, 2019. IEEE

2018

Learning to Hash for Large-Scale Medical Image Retrieval. Conjeti, S. Ph.D. Thesis, Technical University Munich, Germany, 2018.
 

Abstract: Deep Hashing for Large-Scale Medical Image Retrieval. Conjeti, S.; Paschali, M.; Roy, A. G.; and Navab, N. In Maier, A. K.; Deserno, T. M.; Handels, H.; Maier-Hein, K. H.; Palm, C.; and Tolxdorff, T., editor(s), Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen, of Informatik Aktuell, pages 35, 2018. Springer Vieweg
 

Abstract: Fast MRI Whole Brain Segmentation with Fully Convolutional Neural Networks. Roy, A. G.; Conjeti, S.; Navab, N.; and Wachinger, C. In Maier, A. K.; Deserno, T. M.; Handels, H.; Maier-Hein, K. H.; Palm, C.; and Tolxdorff, T., editor(s), Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen, of Informatik Aktuell, pages 42, 2018. Springer Vieweg 

Human Motion Analysis with Deep Metric Learning. Coskun, H.; Tan, D. J.; Conjeti, S.; Navab, N.; and Tombari, F. In Ferrari, V.; Hebert, M.; Sminchisescu, C.; and Weiss, Y., editor(s), Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIV, volume 11218, of Lecture Notes in Computer Science, pages 693–710, 2018. Springer
 

InfiNet: Fully convolutional networks for infant brain MRI segmentation. Kumar, S.; Conjeti, S.; Roy, A. G.; Wachinger, C.; and Navab, N. In 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, DC, USA, April 4-7, 2018, pages 145–148, 2018. IEEE  

 

Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification. Das, K.; Conjeti, S.; Roy, A. G.; Chatterjee, J.; and Sheet, D. In 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, DC, USA, April 4-7, 2018, pages 578–581, 2018. IEEE 

Complex Fully Convolutional Neural Networks for MR Image Reconstruction. Dedmari, M. A.; Conjeti, S.; Estrada, S.; Ehses, P.; Stöcker, T.; and Reuter, M. In Knoll, F.; Maier, A. K.; and Rueckert, D., editor(s), Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings, volume 11074, of Lecture Notes in Computer Science, pages 30–38, 2018. Springer
 

Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks. Estrada, S.; Conjeti, S.; Dedmari, M. A.; Navab, N.; and Reuter, M. In Shi, Y.; Suk, H.; and Liu, M., editor(s), Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings, volume 11046, of Lecture Notes in Computer Science, pages 214–222, 2018. Springer 

 

Learning Optimal Deep Projection of 1818F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes. Kumar, S.; Roy, A. G.; Wu, P.; Conjeti, S.; Anand, R. S.; Wang, J.; Yakushev, I.; Förster, S.; Schwaiger, M.; Huang, S.; Rominger, A.; Zuo, C.; and Shi, K. In Stoyanov, D.; Taylor, Z.; Carneiro, G.; Syeda-Mahmood, T. F.; Martel, A. L.; Maier-Hein, L.; Tavares, J. M. R. S.; Bradley, A. P.; Papa, J. P.; Belagiannis, V.; Nascimento, J. C.; Lu, Z.; Conjeti, S.; Moradi, M.; Greenspan, H.; and Madabhushi, A., editor(s), Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings, volume 11045, of Lecture Notes in Computer Science, pages 227–235, 2018. Springer
 

Webly Supervised Learning for Skin Lesion Classification. Navarro, F.; Conjeti, S.; Tombari, F.; and Navab, N. In Frangi, A. F.; Schnabel, J. A.; Davatzikos, C.; Alberola-López, C.; and Fichtinger, G., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II, volume 11071, of Lecture Notes in Computer Science, pages 398–406, 2018. Springer
 

Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples. Paschali, M.; Conjeti, S.; Navarro, F.; and Navab, N. In Frangi, A. F.; Schnabel, J. A.; Davatzikos, C.; Alberola-López, C.; and Fichtinger, G., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, volume 11070, of Lecture Notes in Computer Science, pages 493–501, 2018. Springer
 

Hashing-Based Atlas Ranking and Selection for Multiple-Atlas Segmentation. Katouzian, A.; Wang, H.; Conjeti, S.; Tang, H.; Dehghan, E.; Karargyris, A.; Pillai, A.; Clarkson, K.; and Navab, N. In Frangi, A. F.; Schnabel, J. A.; Davatzikos, C.; Alberola-López, C.; and Fichtinger, G., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV, volume 11073, of Lecture Notes in Computer Science, pages 543–551, 2018. Springer
 

Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling. Roy, A. G.; Conjeti, S.; Navab, N.; and Wachinger, C. In Frangi, A. F.; Schnabel, J. A.; Davatzikos, C.; Alberola-López, C.; and Fichtinger, G., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, volume 11070, of Lecture Notes in Computer Science, pages 664–672, 2018. Springer
 

Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. Stoyanov, D.; Taylor, Z.; Carneiro, G.; Syeda-Mahmood, T. F.; Martel, A. L.; Maier-Hein, L.; Tavares, J. M. R. S.; Bradley, A. P.; Papa, J. P.; Belagiannis, V.; Nascimento, J. C.; Lu, Z.; Conjeti, S.; Moradi, M.; Greenspan, H.; and Madabhushi, A., editors. Volume 11045, of Lecture Notes in Computer Science.Springer. 2018.
 

SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis. Gunashekar, D.; Conjeti, S.; Roy, A. G.; Navab, N.; and Shi, K. CoRR, abs/1806.11475. 2018. 
 

2017

Coupled Manifold Learning for Retrieval Across Modalities. Kazi, A.; Conjeti, S.; Katouzian, A.; and Navab, N. In 2017 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2017, Venice, Italy, October 22-29, 2017, pages 1321–1328, 2017. IEEE Computer Society
 

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data. Roy, A. G.; Conjeti, S.; Sheet, D.; Katouzian, A.; Navab, N.; and Wachinger, C. In Descoteaux, M.; Maier-Hein, L.; Franz, A. M.; Jannin, P.; Collins, D. L.; and Duchesne, S., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 - 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III, volume 10435, of Lecture Notes in Computer Science, pages 231–239, 2017. Springer
 

Hashing with Residual Networks for Image Retrieval. Conjeti, S.; Roy, A. G.; Katouzian, A.; and Navab, N. In Descoteaux, M.; Maier-Hein, L.; Franz, A. M.; Jannin, P.; Collins, D. L.; and Duchesne, S., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 - 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III, volume 10435, of Lecture Notes in Computer Science, pages 541–549, 2017. Springer
 

Deep Multiple Instance Hashing for Scalable Medical Image Retrieval. Conjeti, S.; Paschali, M.; Katouzian, A.; and Navab, N. In Descoteaux, M.; Maier-Hein, L.; Franz, A. M.; Jannin, P.; Collins, D. L.; and Duchesne, S., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 - 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III, volume 10435, of Lecture Notes in Computer Science, pages 550–558, 2017. Springer
 

Learning Robust Hash Codes for Multiple Instance Image Retrieval. Conjeti, S.; Paschali, M.; Katouzian, A.; and Navab, N. CoRR, abs/1703.05724. 2017.
 

ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network. Roy, A. G.; Conjeti, S.; Karri, S. P. K.; Sheet, D.; Katouzian, A.; Wachinger, C.; and Navab, N. CoRR, abs/1704.02161. 2017.
 

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data. Roy, A. G.; Conjeti, S.; Sheet, D.; Katouzian, A.; Navab, N.; and Wachinger, C. CoRR, abs/1705.00938. 2017.
 

2016

Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection. Pölsterl, S.; Conjeti, S.; Navab, N.; and Katouzian, A. Artif. Intell. Medicine, 72: 1–11. 2016.
 

Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization. Conjeti, S.; Katouzian, A.; Roy, A. G.; Peter, L.; Sheet, D.; Carlier, S. G.; Laine, A.; and Navab, N. Medical Image Anal., 32: 1–17. 2016.
 

Metric hashing forests. Conjeti, S.; Katouzian, A.; Kazi, A.; Mesbah, S.; Beymer, D.; Syeda-Mahmood, T. F.; and Navab, N. Medical Image Anal., 34: 13–29. 2016.
 

Neuron-Miner: An Advanced Tool for Morphological Search and Retrieval in Neuroscientific Image Databases. Conjeti, S.; Mesbah, S.; Negahdar, M.; Rautenberg, P. L.; Zhang, S.; Navab, N.; and Katouzian, A. Neuroinformatics, 14(4): 369–385. 2016. 

Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks. Roy, A. G.; Conjeti, S.; Carlier, S. G.; Dutta, P. K.; Kastrati, A.; Laine, A. F.; Navab, N.; Katouzian, A.; and Sheet, D. IEEE J. Biomed. Health Informatics, 20(2): 606–614. 2016.  

Maximum inner product search for morphological retrieval of large-scale neuron data. Li, Z.; Shen, F.; Fang, R.; Conjeti, S.; Katouzian, A.; and Zhang, S. In 13th IEEE International Symposium on Biomedical Imaging, ISBI 2016, Prague, Czech Republic, April 13-16, 2016, pages 602–606, 2016. IEEE
 

Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography. Roy, A. G.; Conjeti, S.; Carlier, S. G.; Houissa, K.; König, A.; Dutta, P. K.; Laine, A. F.; Navab, N.; Katouzian, A.; and Sheet, D. In 13th IEEE International Symposium on Biomedical Imaging, ISBI 2016, Prague, Czech Republic, April 13-16, 2016, pages 1359–1362, 2016. IEEE
 

Deeply learnt hashing forests for content based image retrieval in prostate MR images. Shah, A.; Conjeti, S.; Navab, N.; and Katouzian, A. In Styner, M. A.; and Angelini, E. D., editor(s), Medical Imaging 2016: Image Processing, San Diego, California, USA, February 27, 2016, volume 9784, of SPIE Proceedings, pages 978414, 2016. SPIE
 

Deep Residual Hashing. Conjeti, S.; Roy, A. G.; Katouzian, A.; and Navab, N. CoRR, abs/1612.05400. 2016.
 

Cross-Modal Manifold Learning for Cross-modal Retrieval. Conjeti, S.; Kazi, A.; Navab, N.; and Katouzian, A. CoRR, abs/1612.06098. 2016.
 

2015

Multi-scale Graph-based Guided Filter for De-noising Cryo-Electron Tomographic Data. Albarqouni, S.; Baust, M.; Conjeti, S.; Al-Amoudi, A.; and Navab, N. In Xie, X.; Jones, M. W.; and Tam, G. K. L., editor(s), Proceedings of the British Machine Vision Conference 2015, BMVC 2015, Swansea, UK, September 7-10, 2015, pages 17.1–17.10, 2015. BMVA Press
 

Deformable registration of immunofluorescence and histology using iterative cross-modal propagation. Conjeti, S.; Yigitsoy, M.; Peng, T.; Sheet, D.; Chatterjee, J.; Bayer, C.; Navab, N.; and Katouzian, A. In 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, NY, USA, April 16-19, 2015, pages 310–313, 2015. IEEE
 

Bag of forests for modelling of tissue energy interaction in optical coherence tomography for atherosclerotic plaque susceptibility assessment. Roy, A. G.; Conjeti, S.; Carlier, S. G.; König, A.; Kastrati, A.; Dutta, P. K.; Laine, A. F.; Navab, N.; Sheet, D.; and Katouzian, A. In 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, NY, USA, April 16-19, 2015, pages 428–431, 2015. IEEE
 

Mutually coherent structural representation for image registration through joint manifold embedding and alignment. Conjeti, S.; Yigitsoy, M.; Sheet, D.; Chatterjee, J.; Navab, N.; and Katouzian, A. In 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, NY, USA, April 16-19, 2015, pages 601–604, 2015. IEEE
 

Hashing Forests for Morphological Search and Retrieval in Neuroscientific Image Databases. Mesbah, S.; Conjeti, S.; Kumaraswamy, A.; Rautenberg, P. L.; Navab, N.; and Katouzian, A. In Navab, N.; Hornegger, J.; III, W. M. W.; and Frangi, A. F., editor(s), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5-9, 2015, Proceedings, Part II, volume 9350, of Lecture Notes in Computer Science, pages 135–143, 2015. Springer
 

2014

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios. Singh, R. R.; Conjeti, S.; and Banerjee, R. Int. J. Comput. Intell. Syst., 7(5): 909–923. 2014.
 

Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition. Peng, T.; Wang, L.; Bayer, C.; Conjeti, S.; Baust, M.; and Navab, N. In Golland, P.; Hata, N.; Barillot, C.; Hornegger, J.; and Howe, R. D., editor(s), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 - 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I, volume 8673, of Lecture Notes in Computer Science, pages 33–40, 2014. Springer
 

Full-Wave Intravascular Ultrasound Simulation from Histology. Kraft, S.; Conjeti, S.; Noël, P. B.; Carlier, S. G.; Navab, N.; and Katouzian, A. In Golland, P.; Hata, N.; Barillot, C.; Hornegger, J.; and Howe, R. D., editor(s), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 - 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part II, volume 8674, of Lecture Notes in Computer Science, pages 627–634, 2014. Springer
 

2013

A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals. Singh, R. R.; Conjeti, S.; and Banerjee, R. Biomed. Signal Process. Control., 8(6): 740–754. 2013. 

Detection of retinal vessels in fundus images through transfer learning of tissue specific photon interaction statistical physics. Sheet, D.; Karri, S. P. K.; Conjeti, S.; Ghosh, S.; Chatterjee, J.; and Ray, A. K. In 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013, 7-11 April, 2013, San Francisco, CA, USA, Proceedings, 2013. IEEE