標(biāo)題: Titlebook: Data Engineering in Medical Imaging; Second MICCAI Worksh Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2025 The Editor [打印本頁] 作者: Polk 時間: 2025-3-21 16:27
書目名稱Data Engineering in Medical Imaging影響因子(影響力)
書目名稱Data Engineering in Medical Imaging影響因子(影響力)學(xué)科排名
書目名稱Data Engineering in Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Data Engineering in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Engineering in Medical Imaging被引頻次
書目名稱Data Engineering in Medical Imaging被引頻次學(xué)科排名
書目名稱Data Engineering in Medical Imaging年度引用
書目名稱Data Engineering in Medical Imaging年度引用學(xué)科排名
書目名稱Data Engineering in Medical Imaging讀者反饋
書目名稱Data Engineering in Medical Imaging讀者反饋學(xué)科排名
作者: collagenase 時間: 2025-3-22 00:00
,Evaluating Histopathology Foundation Models for?Few-Shot Tissue Clustering: An?Application to?LC250kage in model training can lead to artificially high metrics that do not genuinely reflect the strength of the approach. The LC25000 dataset, consisting of tissue image tiles extracted from lung and colon samples, is a popular benchmark dataset. In the released version, tissue tiles were augmented r作者: 笨拙的我 時間: 2025-3-22 01:35
,Counterfactual Contrastive Learning: Robust Representations via?Causal Image Synthesis,it is sensitive to the choice of augmentation pipeline. Positive pairs should preserve semantic information while destroying domain-specific information. Standard augmentation pipelines emulate domain-specific changes with pre-defined photometric transformations, but what if we could simulate realis作者: 繼承人 時間: 2025-3-22 05:54
,TTA-OOD: Test-Time Augmentation for?Improving Out-of-Distribution Detection in?Gastrointestinal Visting diagnosis within gastrointestinal settings is the detection of abnormal cases in endoscopic images. Due to the sparsity of data, this process of distinguishing normal from abnormal cases has faced significant challenges, particularly with rare and unseen conditions. To address this issue, we fr作者: ornithology 時間: 2025-3-22 09:43 作者: AVOID 時間: 2025-3-22 16:17
,USegMix: Unsupervised Segment Mix for?Efficient Data Augmentation in?Pathology Images,l technique to mitigate this limitation. In this study, we introduce an efficient data augmentation method for pathology images, called USegMix. Given a set of pathology images, the proposed method generates a new, synthetic image in two phases. In the first phase, USegMix constructs a pool of tissu作者: AVOID 時間: 2025-3-22 18:41
,Synthetic Simplicity: Unveiling Bias in?Medical Data Augmentation,herent statistical characteristics can significantly impact downstream tasks, potentially compromising deployment performance. In this study, we empirically investigate this issue and uncover a critical phenomenon: downstream neural networks often exploit spurious distinctions between real and synth作者: 包庇 時間: 2025-3-22 23:50 作者: 起草 時間: 2025-3-23 05:08 作者: Multiple 時間: 2025-3-23 09:20
,Translating Simulation Images to?X-Ray Images via?Multi-scale Semantic Matching,ators to the real world remains an open problem. The key challenge is the virtual environments are usually not realistically simulated, especially the simulation images. In this paper, we propose a new method to translate simulation images from an endovascular simulator to X-ray images. Previous ima作者: Occipital-Lobe 時間: 2025-3-23 11:35 作者: 斜谷 時間: 2025-3-23 14:39
,Self-Supervised Pretraining for?Cardiovascular Magnetic Resonance Cine Segmentation,ovascular magnetic resonance (CMR) short-axis cine segmentation. However, inconsistent reports of the benefits of SSP for segmentation have made it difficult to apply SSP to CMR. Therefore, this study aimed to evaluate SSP methods for CMR cine segmentation..To this end, short-axis cine stacks of 296作者: 糾纏 時間: 2025-3-23 21:45
,Patient-Level Contrastive Learning for?Enhanced Biomarker Prediction in?Retinal Imaging,etinal images to predict biomarkers for non-retinal diseases such as cardiovascular disease and chronic kidney disease has shown promise. However, despite the success of utilizing retinal images, significant challenges remain. One major issue is the limited availability of retinal images with linked作者: 表示問 時間: 2025-3-24 00:30 作者: 擺動 時間: 2025-3-24 02:32
,Improving NeRF Representation with?No Pose Prior for?Novel View Synthesis in?Colonoscopy,e screening process. A reliable 3D reconstruction of the surveyed area could mitigate those limitations and improve the diagnosis and treatment outcomes. Most 3D reconstruction frameworks rely on two fundamental tasks: a) a reliable camera depth prediction, and b) an accurate camera pose estimation.作者: Trypsin 時間: 2025-3-24 07:48
,Task-Aware Active Learning for?Endoscopic Polyp Segmentation,such a problem is the lack of annotated data. Endoscopic annotations necessitate the specialist knowledge of expert endoscopists, and hence the difficulty of organizing arises along with tremendous costs in time and budget. To address this problem, we investigate an active learning paradigm to reduc作者: jettison 時間: 2025-3-24 13:14 作者: vertebrate 時間: 2025-3-24 16:47 作者: 小平面 時間: 2025-3-24 21:07
Conference proceedings 20257th International conference on?Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in?Marrakesh, Morocco, on October 10, 2024...The 18 papers presented in this book were carefully reviewed and selected. These papers focus on the application of various Data engineering technique作者: parasite 時間: 2025-3-25 00:32 作者: 情感 時間: 2025-3-25 04:13 作者: 龍卷風(fēng) 時間: 2025-3-25 10:04 作者: 催眠 時間: 2025-3-25 13:04 作者: accrete 時間: 2025-3-25 17:45 作者: artless 時間: 2025-3-25 20:09
,Cross-Task Data Augmentation by?Pseudo-Label Generation for?Region Based Coronary Artery Instance Sudo-labels generated on a dataset of separate related task to diversify and improve model performance. This method increases the baseline F1 score by 9% in the validation data set and by 3% in the test data set.作者: WAX 時間: 2025-3-26 01:29
,Real Time Multi Organ Classification on?Computed Tomography Images,tes as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging.作者: 泄露 時間: 2025-3-26 05:18 作者: STENT 時間: 2025-3-26 10:22 作者: 針葉類的樹 時間: 2025-3-26 15:54
Mario Cardano,Marco Castagnettotes as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging.作者: 本能 時間: 2025-3-26 18:21
https://doi.org/10.1007/978-3-031-73748-0data augmentation; synthetic data; active learning; medical imaging; data synthesis; federated learning; m作者: 浸軟 時間: 2025-3-27 00:25 作者: STEER 時間: 2025-3-27 04:29 作者: ascetic 時間: 2025-3-27 06:50 作者: 開花期女 時間: 2025-3-27 11:45 作者: Trochlea 時間: 2025-3-27 14:32
https://doi.org/10.1007/978-3-7091-5764-0it is sensitive to the choice of augmentation pipeline. Positive pairs should preserve semantic information while destroying domain-specific information. Standard augmentation pipelines emulate domain-specific changes with pre-defined photometric transformations, but what if we could simulate realis作者: 上下倒置 時間: 2025-3-27 20:15
https://doi.org/10.1007/978-3-476-05061-8ting diagnosis within gastrointestinal settings is the detection of abnormal cases in endoscopic images. Due to the sparsity of data, this process of distinguishing normal from abnormal cases has faced significant challenges, particularly with rare and unseen conditions. To address this issue, we fr作者: arboretum 時間: 2025-3-28 00:24
omputer vision remains limited compared to other medical fields like pathology and radiology, primarily due to the scarcity of representative annotated data. Whereas transfer learning from large annotated datasets such as ImageNet has been conventionally the norm to achieve high-performing models, r作者: 他日關(guān)稅重重 時間: 2025-3-28 04:24
Liang the Moral and Social Philosopher,l technique to mitigate this limitation. In this study, we introduce an efficient data augmentation method for pathology images, called USegMix. Given a set of pathology images, the proposed method generates a new, synthetic image in two phases. In the first phase, USegMix constructs a pool of tissu作者: Heretical 時間: 2025-3-28 08:47 作者: 小臼 時間: 2025-3-28 13:06 作者: 被告 時間: 2025-3-28 17:40
In this paper, we present the EndoDepth benchmark, an evaluation framework designed to assess the robustness of monocular depth prediction models in endoscopic scenarios. Unlike traditional datasets, the EndoDepth benchmark incorporates common challenges encountered during endoscopic procedures. We 作者: 瑣事 時間: 2025-3-28 20:15 作者: assent 時間: 2025-3-29 00:41
ance the effectiveness of real-time processing in AI systems for ultrasound image analysis, a pre-processing step to detect liver views is essential, as many abdominal ultrasound images do not include the liver. In this paper, we introduce a method for efficient liver view classification in ultrasou作者: eardrum 時間: 2025-3-29 06:04 作者: 不朽中國 時間: 2025-3-29 10:50
Artefakte biologischen Ursprungs,etinal images to predict biomarkers for non-retinal diseases such as cardiovascular disease and chronic kidney disease has shown promise. However, despite the success of utilizing retinal images, significant challenges remain. One major issue is the limited availability of retinal images with linked作者: painkillers 時間: 2025-3-29 11:26 作者: FLOAT 時間: 2025-3-29 18:18 作者: disparage 時間: 2025-3-29 23:48
Steuern: Gemeinwohl und Gerechtigkeit,such a problem is the lack of annotated data. Endoscopic annotations necessitate the specialist knowledge of expert endoscopists, and hence the difficulty of organizing arises along with tremendous costs in time and budget. To address this problem, we investigate an active learning paradigm to reduc作者: 杠桿支點 時間: 2025-3-30 03:58 作者: 不怕任性 時間: 2025-3-30 05:28 作者: Fibroid 時間: 2025-3-30 11:32 作者: 懸掛 時間: 2025-3-30 15:20 作者: 解決 時間: 2025-3-30 19:33
,Evaluating Histopathology Foundation Models for?Few-Shot Tissue Clustering: An?Application to?LC250, we create a clean version of LC25000. We then evaluate the quality of features extracted by these foundational models, using the clustering task as a benchmark. Our contributions are: 1) We publicly release our semi-automatic annotation pipeline along with the LC25000-clean dataset to facilitate a作者: 平常 時間: 2025-3-30 22:53 作者: 倒轉(zhuǎn) 時間: 2025-3-31 03:43 作者: Tartar 時間: 2025-3-31 06:48
,Synthetic Simplicity: Unveiling Bias in?Medical Data Augmentation,irst demonstrate this vulnerability on a digit classification task, where the model spuriously utilizes the source of data instead of the digit to provide an inference. We provide further evidence of this phenomenon in a medical imaging problem related to cardiac view classification in echocardiogra作者: creditor 時間: 2025-3-31 12:28
,Pre-processing and?Quality Control of?Large Clinical CT Head Datasets for?Intracranial Arterial Cals (structural similarity index measure) to triage assessment of individual image series. Additionally, we propose superimposing thresholded binary masks of the series to inspect large quantities of data in parallel. We identify and exclude unrecoverable samples and registration failures..In total, o作者: Emmenagogue 時間: 2025-3-31 16:18 作者: 后退 時間: 2025-3-31 19:40 作者: Multiple 時間: 2025-3-31 23:46
,Simple is More: Efficient Liver View Classification in?Ultrasound Images Using Minimal Labeled DataimpleClassifier achieved an accuracy of 91.5% with 257 labeled frames, while ResNet-18 and MLP-Mixer required 801 labeled frames each to achieve 70.2% and 86% accuracy, respectively. These findings demonstrate that combining active learning with an AI classifier, regardless of its complexity, can im