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Thijs Kooi
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2020 – today
- 2024
- [c15]Junha Song
, Tae Soo Kim, Junha Kim, Gunhee Nam
Is User Feedback Always Informative? Retrieval Latent Defending for Semi-supervised Domain Adaptation Without Source Data. ECCV (25) 2024: 72-92 - [c14]Samia Belhadj, Sanguk Park, Ambika Seth, Hesham Dar, Thijs Kooi
Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains. FAIMI/EPIMI@MICCAI 2024: 56-66 - [c13]Laurent Dillard, Hyeonsoo Lee, Weonsuk Lee, Tae Soo Kim, Ali Diba, Thijs Kooi:
SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling. CaPTion@MICCAI 2024: 154-163 - [i11]Junha Song, Tae Soo Kim, Junha Kim, Gunhee Nam, Thijs Kooi, Jaegul Choo:
Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data. CoRR abs/2407.15383 (2024) - [i10]Laurent Dillard, Hyeonsoo Lee, Weonsuk Lee, Tae Soo Kim, Ali Diba, Thijs Kooi:
SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling. CoRR abs/2409.16581 (2024) - [i9]Samia Belhadj, Sanguk Park, Ambika Seth, Hesham Dar, Thijs Kooi:
Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains. CoRR abs/2409.19940 (2024) - 2023
- [c12]Hyeonsoo Lee, Junha Kim, Eunkyung Park, Minjeong Kim, Taesoo Kim, Thijs Kooi:
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images. MICCAI (5) 2023: 389-398 - [i8]Annika Reinke, Minu Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Ación
Understanding metric-related pitfalls in image analysis validation. CoRR abs/2302.01790 (2023) - [i7]Hyeonsoo Lee, Junha Kim, Eunkyung Park, Minjeong Kim, Taesoo Kim, Thijs Kooi:
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images. CoRR abs/2303.15699 (2023) - [i6]Sumin Seo, Jaewoong Shin, Jaewoo Kang, Tae Soo Kim, Thijs Kooi:
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity. CoRR abs/2304.05303 (2023) - 2022
- [c11]Kihyun You
Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography. MICCAI (3) 2022: 55-64 - [c10]Gunhee Nam, Taesoo Kim, Sanghyup Lee
OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs. AMAI@MICCAI 2022: 139-149 - [c9]Tae Soo Kim, Geonwoon Jang, Sanghyup Lee
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs. MICCAI (3) 2022: 261-270 - [i5]Thijs Kooi:
Tutorial on the development of AI models for medical image analysis. CoRR abs/2208.00766 (2022) - [i4]Tae Soo Kim, Geonwoon Jang, Sanghyup Lee, Thijs Kooi:
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs. CoRR abs/2209.15314 (2022) - [i3]Gunhee Nam, Taesoo Kim, Sanghyup Lee, Thijs Kooi:
OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs. CoRR abs/2210.06806 (2022)
2010 – 2019
- 2017
- [j2]Thijs Kooi
Large scale deep learning for computer aided detection of mammographic lesions. Medical Image Anal. 35: 303-312 (2017) - [j1]Geert Litjens
A survey on deep learning in medical image analysis. Medical Image Anal. 42: 60-88 (2017) - [c8]Thijs Kooi, Nico Karssemeijer:
Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses. Medical Imaging: Computer-Aided Diagnosis 2017: 101341J - [c7]Thijs Kooi, Jan-Jurre Mordang, Nico Karssemeijer:
Conditional random field modelling of interactions between findings in mammography. Medical Imaging: Computer-Aided Diagnosis 2017: 101341E - [i2]Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, Clara I. Sánchez:
A Survey on Deep Learning in Medical Image Analysis. CoRR abs/1702.05747 (2017) - [i1]Thijs Kooi, Nico Karssemeijer:
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks. CoRR abs/1703.07715 (2017) - 2016
- [c6]Jan-Jurre Mordang, Tim Janssen, Alessandro Bria, Thijs Kooi, Albert Gubern-Mérida, Nico Karssemeijer:
Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks. Digital Mammography / IWDM 2016: 35-42 - [c5]Thijs Kooi, Albert Gubern-Mérida, Jan-Jurre Mordang, Ritse Mann, Ruud Pijnappel, Klaas Schuur, Ard den Heeten, Nico Karssemeijer:
A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography. Digital Mammography / IWDM 2016: 51-56 - 2014
- [c4]Thijs Kooi, Nico Karssemeijer:
Invariant Features for Discriminating Cysts from Solid Lesions in Mammography. Digital Mammography / IWDM 2014: 573-580 - [c3]Thijs Kooi, Nico Karssemeijer:
Boosting classification performance in computer aided diagnosis of breast masses in raw full-field digital mammography using processed and screen film images. Medical Imaging: Computer-Aided Diagnosis 2014: 90351B - 2012
- [c2]Thijs Kooi, François de Sorbier, Hideo Saito:
Colour Descriptors for Tracking in Spatial Augmented Reality. ACCV Workshops (2) 2012: 387-399 - 2010
- [c1]Marco A. Wiering, Thijs Kooi:
Region enhanced neural Q-learning for solving model-based POMDPs. IJCNN 2010: 1-8
Coauthor Index
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last updated on 2024-11-07 20:32 CET by the dblp team
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