{"title":"DeepSet SimCLR:用于改进病理表征学习的自监督深度集","authors":"","doi":"10.1016/j.patrec.2024.09.005","DOIUrl":null,"url":null,"abstract":"<div><p>Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures. Although promising approaches, they are significantly more computationally demanding to train, and thus reduce the widespread applicability of these methods away from those with modest computational resources. Thus, in this paper, we aim to improve standard 2D SSL algorithms by modelling the inherent 3D nature of these datasets implicitly. We propose two variants that build upon a strong baseline model and show that both of these variants often outperform the baseline in a variety of downstream tasks. Importantly, in contrast to previous works in both 2D and 3D approaches for 3D medical data, both of our proposals introduce negligible additional overhead in terms of parameter complexity. Although data loading overhead increases over the baseline SimCLR model (which we can show can be somewhat mitigated through parallelisation), our proposed models are still significantly more efficient than previous approaches based on sequence modelling. Overall, our proposed methods help improve the democratisation of these approaches for medical applications.</p></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167865524002617/pdfft?md5=d21c284a16daedc994fdee00b9067807&pid=1-s2.0-S0167865524002617-main.pdf","citationCount":"0","resultStr":"{\"title\":\"DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning\",\"authors\":\"\",\"doi\":\"10.1016/j.patrec.2024.09.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures. Although promising approaches, they are significantly more computationally demanding to train, and thus reduce the widespread applicability of these methods away from those with modest computational resources. Thus, in this paper, we aim to improve standard 2D SSL algorithms by modelling the inherent 3D nature of these datasets implicitly. We propose two variants that build upon a strong baseline model and show that both of these variants often outperform the baseline in a variety of downstream tasks. Importantly, in contrast to previous works in both 2D and 3D approaches for 3D medical data, both of our proposals introduce negligible additional overhead in terms of parameter complexity. Although data loading overhead increases over the baseline SimCLR model (which we can show can be somewhat mitigated through parallelisation), our proposed models are still significantly more efficient than previous approaches based on sequence modelling. Overall, our proposed methods help improve the democratisation of these approaches for medical applications.</p></div>\",\"PeriodicalId\":54638,\"journal\":{\"name\":\"Pattern Recognition Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0167865524002617/pdfft?md5=d21c284a16daedc994fdee00b9067807&pid=1-s2.0-S0167865524002617-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167865524002617\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167865524002617","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning
Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures. Although promising approaches, they are significantly more computationally demanding to train, and thus reduce the widespread applicability of these methods away from those with modest computational resources. Thus, in this paper, we aim to improve standard 2D SSL algorithms by modelling the inherent 3D nature of these datasets implicitly. We propose two variants that build upon a strong baseline model and show that both of these variants often outperform the baseline in a variety of downstream tasks. Importantly, in contrast to previous works in both 2D and 3D approaches for 3D medical data, both of our proposals introduce negligible additional overhead in terms of parameter complexity. Although data loading overhead increases over the baseline SimCLR model (which we can show can be somewhat mitigated through parallelisation), our proposed models are still significantly more efficient than previous approaches based on sequence modelling. Overall, our proposed methods help improve the democratisation of these approaches for medical applications.
期刊介绍:
Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.