Search Results for author: Daniel Haehn

Found 11 papers, 4 papers with code

Web-based Melanoma Detection

no code implementations22 Mar 2024 SangHyuk Kim, Edward Gaibor, Daniel Haehn

Melanoma is the most aggressive form of skin cancer, and early detection can significantly increase survival rates and prevent cancer spread.

Lesion Search with Self-supervised Learning

no code implementations18 Nov 2023 Kristin Qi, Jiali Cheng, Daniel Haehn

Content-based image retrieval (CBIR) with self-supervised learning (SSL) accelerates clinicians' interpretation of similar images without manual annotations.

Content-Based Image Retrieval Contrastive Learning +2

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

1 code implementation30 Aug 2023 Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger

For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.

Anatomy Mixed Reality

SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment

1 code implementation10 May 2023 Loraine Franke, Tae Young Park, Jie Luo, Yogesh Rathi, Steve Pieper, Lipeng Ning, Daniel Haehn

We present a real-time visualization system for Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique for treating various brain disorders and mental health diseases.

Computational Efficiency

AutoDOViz: Human-Centered Automation for Decision Optimization

no code implementations19 Feb 2023 Daniel Karl I. Weidele, Shazia Afzal, Abel N. Valente, Cole Makuch, Owen Cornec, Long Vu, Dharmashankar Subramanian, Werner Geyer, Rahul Nair, Inge Vejsbjerg, Radu Marinescu, Paulito Palmes, Elizabeth M. Daly, Loraine Franke, Daniel Haehn

AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.

AutoML reinforcement-learning +1

TRAKO: Efficient Transmission of Tractography Data for Visualization

1 code implementation26 Apr 2020 Daniel Haehn, Loraine Franke, Fan Zhang, Suheyla Cetin Karayumak, Steve Pieper, Lauren O'Donnell, Yogesh Rathi

Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines.

Fast Mitochondria Detection for Connectomics

no code implementations MIDL 2019 Vincent Casser, Kai Kang, Hanspeter Pfister, Daniel Haehn

High-resolution connectomics data allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar.

Guided Proofreading of Automatic Segmentations for Connectomics

no code implementations CVPR 2018 Daniel Haehn, Verena Kaynig, James Tompkin, Jeff W. Lichtman, Hanspeter Pfister

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading.

Image Segmentation Segmentation +1

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