NK871
发表于 2025-3-21 16:31:45
书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0941734<br><br> <br><br>书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0941734<br><br> <br><br>
放逐某人
发表于 2025-3-21 22:08:33
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聪明
发表于 2025-3-22 04:00:21
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谄媚于性
发表于 2025-3-22 08:04:59
Understanding and Interpreting Machine Learning in Medical Image Computing Applications978-3-030-02628-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
不利
发表于 2025-3-22 11:19:18
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Ise labels and non-expert similarity. Results are improved over baselines trained on disease labels alone, as well as standard multiclass loss. Quantitative relevance of results, according to non-expert similarity, as well as localized image regions, are also significantly improved.
BUMP
发表于 2025-3-22 13:34:00
0302-97432018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Inte
Cocker
发表于 2025-3-22 17:43:37
Towards Robust CT-Ultrasound Registration Using Deep Learning Methodsially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.
critique
发表于 2025-3-22 21:52:28
Exploring Adversarial Examples medical problem namely pose estimation of surgical tools into its barest form. An analytical decision boundary and exhaustive search of the one-pixel attack across multiple image dimensions let us localize the regions of frequent successful one-pixel attacks at the image space.
欢乐东方
发表于 2025-3-23 03:20:21
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attackshite- and black-box), namely gradient-based, score-based, and decision-based attacks. Furthermore, we modified the pooling operations in the two classification networks to measure their sensitivities against different attacks, on the specific task of chest X-ray classification.
不透明性
发表于 2025-3-23 05:58:44
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classificationlearned feature maps, in the field of dermatology. We show that, to some extent, CNNs focus on features similar to those used by dermatologists to make a diagnosis. However, more research is required for fully explaining their output.