Titlebook: Data Engineering in Medical Imaging; Second MICCAI Worksh Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2025 The Editor

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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

collagenase

,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

笨拙的我

,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

繼承人

,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

ornithology

AVOID

,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

AVOID

,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

包庇

起草

Multiple

,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