Proceedings of machine learning research最新文献_第2页

Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning. 基于不确定性的非对称互惠学习桥接联邦学习模型异质性。

Proceedings of machine learning research Pub Date : 2024-07-01

Jiaqi Wang, Chenxu Zhao, Lingjuan Lyu, Quanzeng You, Mengdi Huai, Fenglong Ma

引用次数: 0

Adapt and Diffuse: Sample-Adaptive Reconstruction Via Latent Diffusion Models. 适应与扩散：通过潜在扩散模型进行样本适应性重建。

Proceedings of machine learning research Pub Date : 2024-07-01

Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi

{"title":"Adapt and Diffuse: Sample-Adaptive Reconstruction Via Latent Diffusion Models.","authors":"Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi","doi":"","DOIUrl":"","url":null,"abstract":"Inverse problems arise in a multitude of applications, where the goal is to recover a clean signal from noisy and possibly (non)linear observations. The difficulty of a reconstruction problem depends on multiple factors, such as the structure of the ground truth signal, the severity of the degradation and the complex interactions between the above. This results in natural sample-by-sample variation in the difficulty of a reconstruction task, which is often overlooked by contemporary techniques. Our key observation is that most existing inverse problem solvers lack the ability to adapt their compute power to the difficulty of the reconstruction task, resulting in subpar performance and wasteful resource allocation. We propose a novel method that we call severity encoding, to estimate the degradation severity of noisy, degraded signals in the latent space of an autoencoder. We show that the estimated severity has strong correlation with the true corruption level and can give useful hints at the difficulty of reconstruction problems on a sample-by-sample basis. Furthermore, we propose a reconstruction method based on latent diffusion models that leverages the predicted degradation severities to fine-tune the reverse diffusion sampling trajectory and thus achieve sample-adaptive inference times. Our framework acts as a wrapper that can be combined with any latent diffusion-based baseline solver, imbuing it with sample-adaptivity and acceleration. We perform numerical experiments on both linear and nonlinear inverse problems and demonstrate that our technique greatly improves the performance of the baseline solver and achieves up to 10× acceleration in mean sampling speed.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"12723-12753"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11421836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142334004","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

Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources. 利用部分数据源进行临床结果预测的对比学习

Proceedings of machine learning research Pub Date : 2024-07-01

Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao

{"title":"Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.","authors":"Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao","doi":"","DOIUrl":"","url":null,"abstract":"The use of machine learning models to predict clinical outcomes from (longitudinal) electronic health record (EHR) data is becoming increasingly popular due to advances in deep architectures, representation learning, and the growing availability of large EHR datasets. Existing models generally assume access to the same data sources during both training and inference stages. However, this assumption is often challenged by the fact that real-world clinical datasets originate from various data sources (with distinct sets of covariates), which though can be available for training (in a research or retrospective setting), are more realistically only partially available (a subset of such sets) for inference when deployed. So motivated, we introduce Contrastive Learning for clinical Outcome Prediction with Partial data Sources (CLOPPS), that trains encoders to capture information across different data sources and then leverages them to build classifiers restricting access to a single data source. This approach can be used with existing cross-sectional or longitudinal outcome classification models. We present experiments on two real-world datasets demonstrating that CLOPPS consistently outperforms strong baselines in several practical scenarios.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"54156-54177"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989752","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

DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency. DiracDiffusion：去噪和增量重建，确保数据一致性

Proceedings of machine learning research Pub Date : 2024-07-01

Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi

{"title":"DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency.","authors":"Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi","doi":"","DOIUrl":"","url":null,"abstract":"Diffusion models have established new state of the art in a multitude of computer vision tasks, including image restoration. Diffusion-based inverse problem solvers generate reconstructions of exceptional visual quality from heavily corrupted measurements. However, in what is widely known as the perception-distortion trade-off, the price of perceptually appealing reconstructions is often paid in declined distortion metrics, such as PSNR. Distortion metrics measure faithfulness to the observation, a crucial requirement in inverse problems. In this work, we propose a novel framework for inverse problem solving, namely we assume that the observation comes from a stochastic degradation process that gradually degrades and noises the original clean image. We learn to reverse the degradation process in order to recover the clean image. Our technique maintains consistency with the original measurement throughout the reverse process, and allows for great flexibility in trading off perceptual quality for improved distortion metrics and sampling speedup via early-stopping. We demonstrate the efficiency of our method on different high-resolution datasets and inverse problems, achieving great improvements over other state-of-the-art diffusion-based methods with respect to both perceptual and distortion metrics.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"12754-12783"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482675","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

Self-supervised pretraining in the wild imparts image acquisition robustness to medical image transformers: an application to lung cancer segmentation. 野外自监督预训练赋予医学图像转换器图像获取鲁棒性：肺癌分割的应用。

Proceedings of machine learning research Pub Date : 2024-07-01

Jue Jiang, Harini Veeraraghavan

{"title":"Self-supervised pretraining in the wild imparts image acquisition robustness to medical image transformers: an application to lung cancer segmentation.","authors":"Jue Jiang, Harini Veeraraghavan","doi":"","DOIUrl":"","url":null,"abstract":"Self-supervised learning (SSL) is an approach to pretrain models with unlabeled datasets and extract useful feature representations such that these models can be easily fine-tuned for various downstream tasks. Self-pretraining applies SSL on curated task-specific datasets without using task-specific labels. Increasing availability of public data repositories has now made it possible to utilize diverse and large, task unrelated datasets to pretrain models in the \"wild\" using SSL. However, the benefit of such wild-pretraining over self-pretraining has not been studied in the context of medical image analysis. Hence, we analyzed transformers (Swin and ViT) and a convolutional neural network created using wild- and self-pretraining trained to segment lung tumors from 3D-computed tomography (CT) scans in terms of: (a) accuracy, (b) fine-tuning epoch efficiency, and (c) robustness to image acquisition differences (contrast versus non-contrast, slice thickness, and image reconstruction kernels). We also studied feature reuse using centered kernel alignment (CKA) with the Swin networks. Our analysis with two independent testing (public N = 139; internal N = 196) datasets showed that wild-pretrained Swin models significantly outperformed self-pretrained Swin for the various imaging acquisitions. Fine-tuning epoch efficiency was higher for both wild-pretrained Swin and ViT models compared to their self-pretrained counterparts. Feature reuse close to the final encoder layers was lower than in the early layers for wild-pretrained models irrespective of the pretext tasks used in SSL. Models and code will be made available through GitHub upon manuscript acceptance.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"250 ","pages":"708-721"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017993","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

From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions. 从有偏见的选择标签到伪标签：从有偏见的决策中学习的期望最大化框架。

Proceedings of machine learning research Pub Date : 2024-07-01

Trenton Chang, Jenna Wiens

引用次数: 0

Network conditioning for synergistic learning on partial annotations. 部分注释协同学习的网络调节。

Proceedings of machine learning research Pub Date : 2024-07-01

Benjamin Billot, Neel Dey, Esra Abaci Turk, P Ellen Grant, Polina Golland

{"title":"Network conditioning for synergistic learning on partial annotations.","authors":"Benjamin Billot, Neel Dey, Esra Abaci Turk, P Ellen Grant, Polina Golland","doi":"","DOIUrl":"","url":null,"abstract":"The robustness and accuracy of multi-organ segmentation networks is limited by the scarcity of labels. A common strategy to alleviate the annotation burden is to use partially labelled datasets, where each image can be annotated for a subset of all organs of interest. Unfortunately, this approach causes inconsistencies in the background class since it can now include target organs. Moreover, we consider the even more relaxed setting of region-based segmentation, where voxels can be labelled for super-regions, thus causing further inconsistencies across annotations. Here we propose CoNeMOS (Conditional Network for Multi-Organ Segmentation), a framework that leverages a label-conditioned network for synergistic learning on partially labelled region-based segmentations. Conditioning is achieved by combining convolutions with expressive Feature-wise Linear Modulation (FiLM) layers, whose parameters are controlled by an auxiliary network. In contrast to other conditioning methods, FiLM layers are stable to train and add negligible computation overhead, which enables us to condition the entire network. As a result, the network can learn where it needs to extract shared or label-specific features, instead of imposing it with the architecture (e.g., with different segmentation heads). By encouraging flexible synergies across labels, our method obtains state-of-the-art results for the segmentation of challenging low-resolution fetal MRI data. Our code is available at https://github.com/BBillot/CoNeMOS.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"250 ","pages":"119-130"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981797","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

From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR. 从基本到额外的功能：超图变压器预训练-然后微调平衡临床预测电子病历。

Proceedings of machine learning research Pub Date : 2024-06-01

Ran Xu, Yiwen Lu, Chang Liu, Yong Chen, Yan Sun, Xiao Hu, Joyce C Ho, Carl Yang

引用次数: 0

Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges. 利用 LLM 从电子病历中检索证据：可能性与挑战。

Proceedings of machine learning research Pub Date : 2024-06-01

Hiba Ahsan, Denis Jered McInerney, Jisoo Kim, Christopher Potter, Geoffrey Young, Silvio Amir, Byron C Wallace

{"title":"Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges.","authors":"Hiba Ahsan, Denis Jered McInerney, Jisoo Kim, Christopher Potter, Geoffrey Young, Silvio Amir, Byron C Wallace","doi":"","DOIUrl":"","url":null,"abstract":"Unstructured data in Electronic Health Records (EHRs) often contains critical information-complementary to imaging-that could inform radiologists' diagnoses. But the large volume of notes often associated with patients together with time constraints renders manually identifying relevant evidence practically infeasible. In this work we propose and evaluate a zero-shot strategy for using LLMs as a mechanism to efficiently retrieve and summarize unstructured evidence in patient EHR relevant to a given query. Our method entails tasking an LLM to infer whether a patient has, or is at risk of, a particular condition on the basis of associated notes; if so, we ask the model to summarize the supporting evidence. Under expert evaluation, we find that this LLM-based approach provides outputs consistently preferred to a pre-LLM information retrieval baseline. Manual evaluation is expensive, so we also propose and validate a method using an LLM to evaluate (other) LLM outputs for this task, allowing us to scale up evaluation. Our findings indicate the promise of LLMs as interfaces to EHR, but also highlight the outstanding challenge posed by \"hallucinations\". In this setting, however, we show that model confidence in outputs strongly correlates with faithful summaries, offering a practical means to limit confabulations.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"248 ","pages":"489-505"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121291","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

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation. 用于体育锻炼数据估算的时空多尺度稀疏自我关注。

Proceedings of machine learning research Pub Date : 2024-06-01

Hui Wei, Maxwell A Xu, Colin Samplawski, James M Rehg, Santosh Kumar, Benjamin M Marlin

引用次数: 0