Data engineering in medical imaging : first MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. DEMI (Workshop) (1st : 2023 : Vancouver, B.C.)最新文献

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Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining. 仅使用自我监督预训练改进噪声标签中的医学图像分类

Data engineering in medical imaging : first MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. DEMI (Workshop) (1st : 2023 : Vancouver, B.C.) Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-44992-5_8

Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Cristian A Linte

{"title":"Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining.","authors":"Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Cristian A Linte","doi":"10.1007/978-3-031-44992-5_8","DOIUrl":"10.1007/978-3-031-44992-5_8","url":null,"abstract":"Noisy labels hurt deep learning-based supervised image classification performance as the models may overfit the noise and learn corrupted feature extractors. For natural image classification training with noisy labeled data, model initialization with contrastive self-supervised pretrained weights has shown to reduce feature corruption and improve classification performance. However, no works have explored: i) how other self-supervised approaches, such as pretext task-based pretraining, impact the learning with noisy label, and ii) any self-supervised pretraining methods alone for medical images in noisy label settings. Medical images often feature smaller datasets and subtle inter-class variations, requiring human expertise to ensure correct classification. Thus, it is not clear if the methods improving learning with noisy labels in natural image datasets such as CIFAR would also help with medical images. In this work, we explore contrastive and pretext task-based selfsupervised pretraining to initialize the weights of a deep learning classification model for two medical datasets with self-induced noisy labels-NCT-CRC-HE-100K tissue histological images and COVID-QU-Ex chest X-ray images. Our results show that models initialized with pretrained weights obtained from self-supervised learning can effectively learn better features and improve robustness against noisy labels.","PeriodicalId":520016,"journal":{"name":"Data engineering in medical imaging : first MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. DEMI (Workshop) (1st : 2023 : Vancouver, B.C.)","volume":"14314 ","pages":"78-90"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11321236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating Transformer Encoding Techniques to Improve Data-Driven Volume-to-Surface Liver Registration for Image-Guided Navigation. 研究变压器编码技术，改进用于图像导航的数据驱动的肝脏体表配准。

Michael Young, Zixin Yang, Richard Simon, Cristian A Linte

{"title":"Investigating Transformer Encoding Techniques to Improve Data-Driven Volume-to-Surface Liver Registration for Image-Guided Navigation.","authors":"Michael Young, Zixin Yang, Richard Simon, Cristian A Linte","doi":"10.1007/978-3-031-44992-5_9","DOIUrl":"10.1007/978-3-031-44992-5_9","url":null,"abstract":"Due to limited direct organ visualization, minimally invasive interventions rely extensively on medical imaging and image guidance to ensure accurate surgical instrument navigation and target tissue manipulation. In the context of laparoscopic liver interventions, intra-operative video imaging only provides a limited field-of-view of the liver surface, with no information of any internal liver lesions identified during diagnosis using pre-procedural imaging. Hence, to enhance intra-procedural visualization and navigation, the registration of pre-procedural, diagnostic images and anatomical models featuring target tissues to be accessed or manipulated during surgery entails a sufficient accurate registration of the pre-procedural data into the intra-operative setting. Prior work has demonstrated the feasibility of neural network-based solutions for nonrigid volume-to-surface liver registration. However, view occlusion, lack of meaningful feature landmarks, and liver deformation between the pre- and intra-operative settings all contribute to the difficulty of this registration task. In this work, we leverage some of the state-of-the-art deep learning frameworks to implement and test various network architecture modifications toward improving the accuracy and robustness of volume-to-surface liver registration. Specifically, we focus on the adaptation of a transformer-based segmentation network for the task of better predicting the optimal displacement field for nonrigid registration. Our results suggest that one particular transformer-based network architecture-UTNet-led to significant improvements over baseline performance, yielding a mean displacement error on the order of 4 mm across a variety of datasets.","PeriodicalId":520016,"journal":{"name":"Data engineering in medical imaging : first MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. DEMI (Workshop) (1st : 2023 : Vancouver, B.C.)","volume":"14314 ","pages":"91-101"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11318296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0