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Federated Contrastive Learning for Volumetric Medical Image Segmentation [chapter]

Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu
2021 Lecture Notes in Computer Science  
In this work, we propose an FCL framework for volumetric medical image segmentation with limited annotations.  ...  However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.  ...  contrastive learning for volumetric medical image segmentation with limited annotations.  ... 
doi:10.1007/978-3-030-87199-4_35 fatcat:h5ekgozd45b4vab43djcsxvr6q

Distributed Contrastive Learning for Medical Image Segmentation [article]

Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu
2022 arXiv   pre-print
In this work, we propose two federated self-supervised learning frameworks for volumetric medical image segmentation with limited annotations.  ...  However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.  ...  CONCLUSION This work aims to enable federated contrastive learning (FCL) for volumetric medical image segmentation with limited annotations.  ... 
arXiv:2208.03808v1 fatcat:3ruylvqxkfbzbo7db5oiafsupa

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [article]

Dongnan Liu, Mariano Cabezas, Dongang Wang, Zihao Tang, Lei Bai, Geng Zhan, Yuling Luo, Kain Kyle, Linda Ly, James Yu, Chun-Chien Shieh, Aria Nguyen (+7 others)
2022 arXiv   pre-print
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data.  ...  In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.  ...  Federated Learning Federated learning (FL) provides a decentralized solution for multi-client collaborative learning without raw data sharing.  ... 
arXiv:2205.01509v1 fatcat:nqfoszfdo5b2xaxrlsqincwccm

3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation [article]

Magdalini Paschali, Stefano Gasperini, Abhijit Guha Roy, Michael Y.-S. Fang, Nassir Navab
2019 arXiv   pre-print
We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation.  ...  In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks (F-CNNs), enabling 16x model compression while maintaining performance  ...  TernaryNet [10] was the first attempt in medical imaging to create compact and efficient F-CNNs utilizing ternary weights, where a 2D U-Net was employed for the task of per-slice pancreas CT segmentation  ... 
arXiv:1904.03110v3 fatcat:rgylqodskbcibkrwcllrj2kdpm

Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation [article]

Himashi Peiris, Munawar Hayat, Zhaolin Chen, Gary Egan, Mehrtash Harandi
2022 arXiv   pre-print
Overall, the experimental results verify our method's effectiveness by yielding better performance in segmentation accuracy for each tumor sub-region.  ...  In this concept, we propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features and local distributional smoothness (LDS) during model training  ...  Training Objective The CR-Swin2-VT model's objective is to segment volumetric medical images and it's model learning process is morel image segmentation and the training process is more deeply shared across  ... 
arXiv:2209.07704v1 fatcat:d5vgt3dudjb2rmmwm7y7rjwzrq

Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging

Nima Tajbakhsh, Holger Roth, Demetri Terzopoulos, Jianming Liang
2021 IEEE Transactions on Medical Imaging  
for medical image segmentation," IEEE Trans.  ...  [A4] introduce interactive learning into the few-shot learning strategy for image segmentation.  ... 
doi:10.1109/tmi.2021.3089292 pmid:34795461 pmcid:PMC8594751 fatcat:t7kufjbdyfgazng3gcuyuhawxu

MammoDL: Mammographic Breast Density Estimation using Federated Learning [article]

Ramya Muthukrishnan, Angelina Heyler, Keshava Katti, Sarthak Pati, Walter Mankowski, Aprupa Alahari, Michael Sanborn, Emily F. Conant, Pratik Chaudhari, Despina Kontos, Spyridon Bakas
2022 arXiv   pre-print
Machine learning (ML) models have become the most promising way to quantify breast cancer risk for early, accurate, and equitable diagnoses, but training such models in medical research is often restricted  ...  With the Open Federated Learning (OpenFL) library, this solution enables secure training on datasets across multiple institutions.  ...  this medical imaging task.  ... 
arXiv:2206.05575v2 fatcat:rwjkaq2wpvgrvdlmfsdrxug4qy

Learning to Segment the Lung Volume from CT Scans Based on Semi-Automatic Ground-Truth

Patrick Sousa, Adrian Galdran, Pedro Costa, Aurelio Campilho
2019 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)  
Even if the training set labels are noisy and may contain some errors, we experimentally show that it is possible to learn to accurately segment the lung relying on them.  ...  Numerical comparisons performed on an external test set containing lung segmentations provided by a medical expert demonstrate that the proposed model generalizes well to new data, reaching an average  ...  Partnership Agreement, and the European Regional Development Fund (ERDF), within the project "NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER  ... 
doi:10.1109/isbi.2019.8759309 dblp:conf/isbi/SousaG0C19 fatcat:csdkig3xcrhvdkmog3zajgdzfy

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

Oliver Schoppe, Chenchen Pan, Javier Coronel, Hongcheng Mai, Zhouyi Rong, Mihail Ivilinov Todorov, Annemarie Müskes, Fernando Navarro, Hongwei Li, Ali Ertürk, Bjoern H. Menze
2020 Nature Communications  
Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually.  ...  Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than  ...  Acknowledgements We thank Shan Zhao, Ilgin Kolabas, and Diana Waldmannstetter for helpful discussions throughout the project.  ... 
doi:10.1038/s41467-020-19449-7 pmid:33159057 fatcat:pkei2hyxqncehpdljpk7yfnbba

Privacy-Preserved Federated Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans

Songshang Liu, Howard H. Yang, Yiqi Tao, Yang Feng, Jin Hao, Zuozhu Liu
2022 Frontiers in Communications and Networks  
Our work presents the first attempts of federated learning for 3D tooth segmentation, demonstrating its strong potential in challenging federated 3D medical image analysis in multi-centric settings.  ...  In this study, we propose the FedTSeg framework, a federated 3D tooth segmentation framework with a deep graph convolutional neural network, to resolve the 3D tooth segmentation task while alleviating  ...  ACKNOWLEDGMENTS The authors thank Xiang Li, Mingzhou Wu, and Chenxi Liu for their helpful discussion and initial work on this study.  ... 
doi:10.3389/frcmn.2022.907388 fatcat:tthdemfz4fbzrcsg673jnyf6la

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients [article]

Jakub Nalepa, Krzysztof Kotowski, Bartosz Machura, Szymon Adamski, Oskar Bozek, Bartosz Eksner, Bartosz Kokoszka, Tomasz Pekala, Mateusz Radom, Marek Strzelczak, Lukasz Zarudzki, Agata Krason (+2 others)
2022 arXiv   pre-print
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma.  ...  In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients.  ...  (Future Processing Healthcare) for their valuable help in managing this study.  ... 
arXiv:2209.01402v1 fatcat:ycnpaawmqndfbnfi2d7erxukze

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Reza Kalantar, Gigin Lin, Jessica M. Winfield, Christina Messiou, Susan Lalondrelle, Matthew D. Blackledge, Dow-Mu Koh
2021 Diagnostics  
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing  ...  DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology.  ...  We conducted online search with the keywords "deep learning" and "medical image segmentation" on Google Scholar for studies published between January 2016 and December 2020.  ... 
doi:10.3390/diagnostics11111964 pmid:34829310 pmcid:PMC8625809 fatcat:alr36jtq6fgeddnluclp5neb2i

Radiomics in neuro-oncological clinical trials

Philipp Lohmann, Enrico Franceschi, Philipp Vollmuth, Frédéric Dhermain, Michael Weller, Matthias Preusser, Marion Smits, Norbert Galldiks
2022 The Lancet Digital Health  
Radiomics is a method to extract undiscovered features from routinely acquired imaging data that can neither be captured by means of human perception nor conventional image analysis.  ...  In patients with brain cancer, radiomics has shown its potential for the non-invasive identification of prognostic biomarkers, automated response assessment, and differentiation between treatment-related  ...  It was shown that a machine-learning model developed by federated learning achieved 99% of the quality of a model that used centralised data. 45 Thus, federated learning might be key for a successful  ... 
doi:10.1016/s2589-7500(22)00144-3 pmid:36182633 fatcat:5mvhn7zb6rbzfp6lgxg6ezvbbe

Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs

Yasheng Chen, Rajat Dhar, Laura Heitsch, Andria Ford, Israel Fernandez-Cadenas, Caty Carrera, Joan Montaner, Weili Lin, Dinggang Shen, Hongyu An, Jin-Moo Lee
2016 NeuroImage: Clinical  
We developed and validated an automated technique for CSF segmentation via integration of random forest (RF) based machine learning with geodesic active contour (GAC) segmentation.  ...  When we applied the algorithm trained from images of one stroke center to segment CTs from another center, similar findings held.  ...  Spanish stroke research network (INVICTUS), Generation Project (PI15/ 01978) and Pre-Test Stroke Project (PMP15/00022) Instituto de Salud Carlos III and Fondo Europeo de Deasarrollo Regional (ISCIII-FEDER  ... 
doi:10.1016/j.nicl.2016.09.018 pmid:27761398 pmcid:PMC5065050 fatcat:i7c5jkao3fcy5j2x36rljpaxvq

Front Matter: Volume 10137

2017 Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging  
Publication of record for individual papers is online in the SPIE Digital Library. Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  LEARNING 10137 2C An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images [10137-83] 10137 2D Machine learning for cardiac  ...  for data-driven vesselness measure [10137-35] 10137 10 Deep learning for brain tumor classification [10137-36] 10137 11 Robust hepatic vessel segmentation using multi deep convolution network [10137  ... 
doi:10.1117/12.2277895 dblp:conf/mibam/X17 fatcat:hq5s7pahirdilkfy4pzali4fe4
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