Titlebook: Understanding and Interpreting Machine Learning in Medical Image Computing Applications; First International Danail Stoyanov,Zeike Taylor,

NK871

书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications影响因子(影响力)




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




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications网络公开度




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications网络公开度学科排名




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications被引频次




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications被引频次学科排名




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications年度引用




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications年度引用学科排名




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications读者反馈




书目名称Understanding and Interpreting Machine Learning in Medical Image Computing Applications读者反馈学科排名

放逐某人

聪明

谄媚于性

Understanding and Interpreting Machine Learning in Medical Image Computing Applications978-3-030-02628-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

不利

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

0302-9743 2018, 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

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

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

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

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.