Machine Learning Science and Technology最新文献

{"title":"Prior guided deep difference meta-learner for fast adaptation to stylized segmentation.","authors":"Dan Nguyen, Anjali Balagopal, Ti Bai, Michael Dohopolski, Mu-Han Lin, Steve Jiang","doi":"10.1088/2632-2153/adc970","DOIUrl":"https://doi.org/10.1088/2632-2153/adc970","url":null,"abstract":"<p><p>Radiotherapy treatment planning requires segmenting anatomical structures in various styles, influenced by guidelines, protocols, preferences, or dose planning needs. Deep learning-based auto-segmentation models, trained on anatomical definitions, may not match local clinicians' styles at new institutions. Adapting these models can be challenging without sufficient resources. We hypothesize that consistent differences between segmentation styles and anatomical definitions can be learned from initial patients and applied to pre-trained models for more precise segmentation. We propose a Prior-guided deep difference meta-learner (DDL) to learn and adapt these differences. We collected data from 440 patients for model development and 30 for testing. The dataset includes contours of the prostate clinical target volume (CTV), parotid, and rectum. We developed a deep learning framework that segments new images with a matching style using example styles as a prior, without model retraining. The pre-trained segmentation models were adapted to three different clinician styles for post-operative CTV for prostate, parotid gland, and rectum segmentation. We tested the model's ability to learn unseen styles and compared its performance with transfer learning, using varying amounts of prior patient style data (0-10 patients). Performance was quantitatively evaluated using dice similarity coefficient (DSC) and Hausdorff distance. With exposure to only three patients for the model, the average DSC (%) improved from 78.6, 71.9, 63.0, 69.6, 52.2 and 46.3-84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTV_style1, CTV_style2, CTV_style3, Parotid_superficial, Rectum_superior, and Rectum_posterior, respectively. The proposed Prior-guided DDL is a fast and effortless network for adapting a structure to new styles. The improved segmentation accuracy may result in reduced contour editing time, providing a more efficient and streamlined clinical workflow.</p>","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 2","pages":"025016"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model.","authors":"Yunxiang Li, Hua-Chieh Shao, Xiaoxue Qian, You Zhang","doi":"10.1088/2632-2153/adc656","DOIUrl":"10.1088/2632-2153/adc656","url":null,"abstract":"<p><p>Diffusion models have demonstrated significant potential in producing high-quality images in medical image translation to aid disease diagnosis, localization, and treatment. Nevertheless, current diffusion models often fall short when it comes to faithfully translating medical images. They struggle to accurately preserve anatomical structures, especially when working with unpaired datasets. In this study, we introduce the Frequency Decoupled Diffusion Model (FDDM) for MR-to-CT conversion. The differences between MR and CT images lie in both anatomical structures (e.g., the outlines of organs or bones) and the data distribution (e.g., intensity values and contrast within). Therefore, FDDM first converts anatomical information using an initial conversion module. Then, the converted anatomical information guides a subsequent diffusion model to generate high-quality CT images. Our diffusion model uses a dual-path reverse diffusion process for low-frequency and high-frequency information, achieving a better balance between image quality and anatomical accuracy. We extensively evaluated FDDM using public datasets for brain MR-to-CT and pelvis MR-to-CT translations. The results show that FDDM outperforms other generative adversarial network (GAN)-based, variational autoencoder (VAE)-based, and diffusion-based models. The evaluation metrics included Fréchet Inception Distance (FID), mean absolute error (MAE), mean squared error (MSE), Structural Similarity Index Measure (SSIM), and Dice similarity coefficient (DICE). FDDM achieved the best scores on all metrics for both datasets, particularly excelling in FID, with scores of 25.9 for brain data and 29.2 for pelvis data, significantly outperforming other methods. These results demonstrate that FDDM can generate high-quality target domain images while maintaining the accuracy of translated anatomical structures, thereby facilitating more precise/accurate downstream tasks including anatomy segmentation and radiotherapy planning.</p>","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 2","pages":""},"PeriodicalIF":4.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12826588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146053995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Image Segmentation Assisted with Clinical Inputs via Language Encoder in A Deep Learning Framework.","authors":"Hengrui Zhao, Biling Wang, Deepkumar Mistry, Jing Wang, Michael Dohopolski, Daniel Yang, Weiguo Lu, Steve Jiang, Dan Nguyen","doi":"10.1088/2632-2153/adb371","DOIUrl":"10.1088/2632-2153/adb371","url":null,"abstract":"<p><strong>Introduction: </strong>Auto-segmentation of tumor volumes and organs at risk (OARs) is a critical step in cancer radiotherapy treatment planning, where rapid, precise adjustments to treatment plans are required to match the patient anatomy. Although auto-segmentation has been clinically accepted for most OARs, auto-segmentation of tumor volumes, particularly clinical target volumes (CTVs), remains a challenge. This difficulty arises because images alone are often insufficient to capture the necessary information for accurate delineation of microscopic tumor invasion invisible on the image itself.</p><p><strong>Methods: </strong>We propose a deep learning-based medical image segmentation framework designed to mimic the clinical process of delineating CTVs and OARs. At its core, the model performs precise segmentation of medical images while enhancing accuracy by integrating clinical information in text format. A transformer-based text encoder converts textual clinical data into vectors, which are incorporated into the segmentation process with image features. This integration bridges the gap between traditional automated segmentation methods and clinician-guided, context-rich delineations. The framework's effectiveness is demonstrated through a prostate segmentation example in the context of radiation therapy for localized prostate cancer, where incorporating clinical context significantly impacts the delineation process.</p><p><strong>Results: </strong>In our experiments, we included additional clinical information potentially influencing clinicians' prostate segmentation. The results show that our proposed method not only outperforms the baseline model, but also surpasses current state-of-the-art methods, with or without clinical contexts. Furthermore, our method demonstrates high performance even with limited data.</p><p><strong>Conclusion: </strong>This proposed segmentation framework has shown to significantly improve auto-segmentation, particularly for CTVs, in cancer radiotherapy.</p>","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12509794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Unsupervised Clustering for Prostate Auto-segmentation With and Without Hydrogel Spacer.","authors":"Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen","doi":"10.1088/2632-2153/ada8f3","DOIUrl":"10.1088/2632-2153/ada8f3","url":null,"abstract":"<p><strong>Introduction: </strong>Clinical datasets for training deep learning (DL) models often exhibit high levels of heterogeneity due to differences such as patient characteristics, new medical techniques, and physician preferences. In recent years, hydrogel spacers have been used in some prostate cancer patients receiving radiotherapy to separate the prostate and the rectum to better spare the rectum while achieving adequate dose coverage on the prostate. However, this substantially affects the CT image appearance, which downstream reduced the contouring accuracy of auto-segmentation algorithms. This leads to highly heterogeneous dataset.</p><p><strong>Methods: </strong>To address this issue, we propose to identify underlying clusters within the dataset and use the cluster labels for segmentation. We collected a clinical dataset of 909 patients, including those with two types of hydrogel spacers and those without. First, we trained a DL model to locate the prostate and limit our field of view to the local area surrounding the prostate and rectum. We then used Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and employed k-means clustering to assign each patient to a cluster. To leverage this clustered data, we propose a text-guided segmentation model, CLIP-UNet, which encodes the cluster information using a text encoder and combines the encoded text information with image features for segmentation.</p><p><strong>Results: </strong>The UMAP results indicated up to three clusters within the dataset. CLIP-UNet with cluster information achieved a Dice score of 86.2% compared to 84.4% from the baseline UNet. Additionally, CLIP-UNet outperforms other state-of-the-art models with or without cluster information.</p><p><strong>Conclusion: </strong>Automatic clustering assisted by deep learning can reveal hidden data clusters in clinical datasets, and CLIP-UNet effectively utilizes clustered labels and achieves higher performance.</p>","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12509332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net.","authors":"Shunyu Yan, Austen Maniscalco, Biling Wang, Dan Nguyen, Steve Jiang, Chenyang Shen","doi":"10.1088/2632-2153/ad829e","DOIUrl":"10.1088/2632-2153/ad829e","url":null,"abstract":"<p><p>In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algorithms are generally time-consuming, reducing the efficiency of ART workflow. This study aims to develop an ultra-fast deep-learning (DL) based secondary dose verification algorithm to accurately estimate dose distributions using computed tomography (CT) and fluence maps (FMs). We integrated FMs into the CT image domain by explicitly resolving the geometry of treatment delivery. For each gantry angle, an FM was constructed based on the optimized multi-leaf collimator apertures and corresponding monitoring units. To effectively encode treatment beam configuration, the constructed FMs were back-projected to <math><mrow><mn>30</mn></mrow> </math> cm away from the isocenter with respect to the exact geometry of the treatment machines. Then, a 3D U-Net was utilized to take the integrated CT and FM volume as input to estimate dose. Training and validation were performed on <math><mrow><mn>381</mn></mrow> </math> prostate cancer cases, with an additional <math><mrow><mn>40</mn></mrow> </math> testing cases for independent evaluation of model performance. The proposed model can estimate dose in ∼ <math><mrow><mn>15</mn></mrow> </math> ms for each patient. The average <i>γ</i> passing rate ( <math><mrow><mn>3</mn> <mi>%</mi> <mrow><mo>/</mo></mrow> <mn>2</mn> <mstyle></mstyle> <mrow><mtext>mm</mtext></mrow> </mrow> </math> , <math><mrow><mn>10</mn> <mi>%</mi></mrow> </math> threshold) for the estimated dose was 99.9% ± 0.15% on testing patients. The mean dose differences for the planning target volume and organs at risk were <math><mrow><mn>0.07</mn> <mi>%</mi> <mo>±</mo> <mn>0.34</mn> <mi>%</mi></mrow> </math> and <math><mrow><mn>0.48</mn> <mi>%</mi> <mo>±</mo> <mn>0.72</mn> <mi>%</mi></mrow> </math> , respectively. We have developed a geometry-resolved DL framework for accurate dose estimation and demonstrated its potential in real-time online ART doses verification.</p>","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"5 4","pages":"045013"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142476443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equivariant tensor network potentials","authors":"M Hodapp and A Shapeev","doi":"10.1088/2632-2153/ad79b5","DOIUrl":"https://doi.org/10.1088/2632-2153/ad79b5","url":null,"abstract":"Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs’ ability of accurately approximating energy landscapes of quantum-mechanical models while being orders of magnitude more computationally efficient. However, the computational cost and number of parameters of many state-of-the-art MLIPs increases exponentially with the number of atomic features. Tensor (non-neural) networks, based on low-rank representations of high-dimensional tensors, have been a way to reduce the number of parameters in approximating multidimensional functions, however, it is often not easy to encode the model symmetries into them. In this work we develop a formalism for rank-efficient equivariant tensor networks (ETNs), i.e. tensor networks that remain invariant under actions of SO(3) upon contraction. All the key algorithms of tensor networks like orthogonalization of cores and DMRG-based algorithms carry over to our equivariant case. Moreover, we show that many elements of modern neural network architectures like message passing, pulling, or attention mechanisms, can in some form be implemented into the ETNs. Based on ETNs, we develop a new class of polynomial-based MLIPs that demonstrate superior performance over existing MLIPs for multicomponent systems.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"4 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing ZX-diagrams with deep reinforcement learning","authors":"Maximilian Nägele and Florian Marquardt","doi":"10.1088/2632-2153/ad76f7","DOIUrl":"https://doi.org/10.1088/2632-2153/ad76f7","url":null,"abstract":"ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a set of local transformation rules that can be applied to them without changing the underlying quantum process they describe. These rules can be exploited to optimize the structure of ZX-diagrams for a range of applications. However, finding an optimal sequence of transformation rules is generally an open problem. In this work, we bring together ZX-diagrams with reinforcement learning, a machine learning technique designed to discover an optimal sequence of actions in a decision-making problem and show that a trained reinforcement learning agent can significantly outperform other optimization techniques like a greedy strategy, simulated annealing, and state-of-the-art hand-crafted algorithms. The use of graph neural networks to encode the policy of the agent enables generalization to diagrams much bigger than seen during the training phase.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"43 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data","authors":"Pranath Reddy, Michael W Toomey, Hanna Parul and Sergei Gleyzer","doi":"10.1088/2632-2153/ad76f8","DOIUrl":"https://doi.org/10.1088/2632-2153/ad76f8","url":null,"abstract":"Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense, a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"70 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Masked particle modeling on sets: towards self-supervised high energy physics foundation models","authors":"Tobias Golling, Lukas Heinrich, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy and John Andrew Raine","doi":"10.1088/2632-2153/ad64a8","DOIUrl":"https://doi.org/10.1088/2632-2153/ad64a8","url":null,"abstract":"We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"75 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming the bootstrap: using transformers to compute scattering amplitudes in planar N =...","authors":"Tianji Cai, Garrett W Merz, François Charton, Niklas Nolte, Matthias Wilhelm, Kyle Cranmer and Lance J Dixon","doi":"10.1088/2632-2153/ad743e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad743e","url":null,"abstract":"We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar Super Yang–Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy ( on both tasks. Our work shows that transformers can be applied successfully to problems in theoretical physics that require exact solutions.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"12 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}