標(biāo)題: Titlebook: Understanding and Interpreting Machine Learning in Medical Image Computing Applications; First International Danail Stoyanov,Zeike Taylor, [打印本頁] 作者: NK871 時(shí)間: 2025-3-21 16:31
書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications影響因子(影響力)
書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications影響因子(影響力)學(xué)科排名
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書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications被引頻次
書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications被引頻次學(xué)科排名
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書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications讀者反饋
書目名稱Understanding and Interpreting Machine Learning in Medical Image Computing Applications讀者反饋學(xué)科排名
作者: 放逐某人 時(shí)間: 2025-3-21 22:08 作者: 聰明 時(shí)間: 2025-3-22 04:00 作者: 諂媚于性 時(shí)間: 2025-3-22 08:04
Understanding and Interpreting Machine Learning in Medical Image Computing Applications978-3-030-02628-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 不利 時(shí)間: 2025-3-22 11:19
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.作者: BUMP 時(shí)間: 2025-3-22 13:34
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作者: Cocker 時(shí)間: 2025-3-22 17:43
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.作者: critique 時(shí)間: 2025-3-22 21:52
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.作者: 歡樂東方 時(shí)間: 2025-3-23 03:20
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.作者: 不透明性 時(shí)間: 2025-3-23 05:58
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.作者: antipsychotic 時(shí)間: 2025-3-23 10:06 作者: 合并 時(shí)間: 2025-3-23 15:48 作者: Brocas-Area 時(shí)間: 2025-3-23 22:05 作者: capillaries 時(shí)間: 2025-3-23 23:18 作者: Admonish 時(shí)間: 2025-3-24 02:24 作者: 不來 時(shí)間: 2025-3-24 08:30 作者: filial 時(shí)間: 2025-3-24 14:35 作者: 發(fā)牢騷 時(shí)間: 2025-3-24 18:31
Exploring Adversarial Examplesd in the context of real-world images with calibrated attacks. To demystify the adversarial examples, rigorous studies need to be designed. Unfortunately, complexity of the medical images hinders such study design directly from the medical images. We hypothesize that adversarial examples might resul作者: Chipmunk 時(shí)間: 2025-3-24 22:42
Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks through surgery. Having accurate segmentation of the ventricular system into its sub-compartments from magnetic resonance images?(MRI) would be beneficial to better characterize the condition of NPH patients. Previous segmentation algorithms need long processing time and often fail to accurately se作者: arcane 時(shí)間: 2025-3-24 23:27
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attackses. However, there is not yet a comprehensive vulnerability analysis of these models against the so-called adversarial perturbations/attacks, which makes deep models more trustful in clinical practices. In this paper, we extensively analyzed the performance of two state-of-the-art classification dee作者: consolidate 時(shí)間: 2025-3-25 06:51 作者: 燒瓶 時(shí)間: 2025-3-25 10:35 作者: 比目魚 時(shí)間: 2025-3-25 12:17
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classificationowever, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why th作者: 宣傳 時(shí)間: 2025-3-25 19:34 作者: Myelin 時(shí)間: 2025-3-25 20:51
Towards Complementary Explanations Using Deep Neural Networksned the attention of the scientific community due to their high accuracy in vast classification problems. However, they are still seen as black-box models where it is hard to understand the reasons for the labels that they generate. This paper proposes a deep model with monotonic constraints that ge作者: 使長胖 時(shí)間: 2025-3-26 02:55
How Users Perceive Content-Based Image Retrieval for Identifying Skin Imageso a specific query image. One application of CBIR in the dermatology domain is displaying a set of visually similar images with a pathology-confirmed diagnosis for a given query skin image. Recently, CBIR algorithms using machine learning with high accuracy rates have gained more attention since res作者: 裝飾 時(shí)間: 2025-3-26 07:48
Conference proceedings 2018 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 Intervention, 作者: 保全 時(shí)間: 2025-3-26 09:12 作者: 有惡臭 時(shí)間: 2025-3-26 16:15 作者: 俗艷 時(shí)間: 2025-3-26 18:09 作者: Loathe 時(shí)間: 2025-3-27 00:07
of bacterial contamination is of great importance. The primary advantage of all luminescence-based assays is their rapidity and sensitivity. Here we describe two different types of luminescence systems that have been adapted for commercial use, bioluminescence (BL) and chemiluminescence (CL). BL is 作者: 抱狗不敢前 時(shí)間: 2025-3-27 02:49 作者: 宿醉 時(shí)間: 2025-3-27 05:22 作者: arthroscopy 時(shí)間: 2025-3-27 09:32 作者: amenity 時(shí)間: 2025-3-27 16:05 作者: osculate 時(shí)間: 2025-3-27 20:54
Yuanyuan Sun,Adriaan Moelker,Wiro J. Niessen,Theo van Walsum and its application to industrial problems. In the experience of the present authors, however, if the subject is to be understood within its true, industrial context it must be taught in relation to the design process. Thus, while this book discusses both modelling and industrial applications, it a作者: 注意到 時(shí)間: 2025-3-28 00:47
Aabhas Majumdar,Raghav Mehta,Jayanthi Sivaswamy.. In our chapter in that Edition, we focused on the nonspecific arm of the cellular immune response (LAK cells, macrophages) and how it could be utilized clinically to cure metastatic cancer [103]. We discussed rapidly developing large scale cell culture technologies used to generate LAK cells and 作者: 使厭惡 時(shí)間: 2025-3-28 05:44 作者: Femine 時(shí)間: 2025-3-28 06:39 作者: 6Applepolish 時(shí)間: 2025-3-28 13:38 作者: 清醒 時(shí)間: 2025-3-28 18:34
Saeid Asgari Taghanaki,Arkadeep Das,Ghassan Hamarnehing & analysis, liquid biopsies, and more.Relates cancer gen.This popular textbook, now in its third edition, provides a theoretical framework for understanding why cancers arise, how they develop and how they can be treated..Particular attention is devoted to the origins of cancer and the applicati作者: Neolithic 時(shí)間: 2025-3-28 21:19 作者: 嚙齒動物 時(shí)間: 2025-3-28 23:18 作者: 彎彎曲曲 時(shí)間: 2025-3-29 04:49
Sérgio Pereira,Raphael Meier,Victor Alves,Mauricio Reyes,Carlos A. Silvao treat patients with cancer, which includes Surgery, Radiation therapy, Chemotherapy, Targeted therapy and Immunotherapy. The efficiency of all these treatments is limited by the capacity of cancer cells to escape therapy. This book explains the mechanisms of anti-cancer drug resistance and strateg作者: 未開化 時(shí)間: 2025-3-29 10:52 作者: EWER 時(shí)間: 2025-3-29 12:06 作者: Cantankerous 時(shí)間: 2025-3-29 17:30 作者: AFFIX 時(shí)間: 2025-3-29 23:14 作者: GET 時(shí)間: 2025-3-30 01:47
Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Datadology to measure the impact of batch effects in classification studies and propose a technique for solving batch effects under the assumption that they are caused by a linear transformation. We empirically show that this approach consistently improve the performance of classifiers in multi-site sce作者: 按時(shí)間順序 時(shí)間: 2025-3-30 07:58
To Learn or Not to Learn Features for Deformable Registration?ld for low level features. This shows that when handcrafted features are designed based on good insights into the problem at hand, they perform better or are comparable to features learnt using deep learning framework.作者: 摘要 時(shí)間: 2025-3-30 09:34 作者: apropos 時(shí)間: 2025-3-30 12:42
Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessmepredicting tumor grade directly from imaging data. In this way, we overcome the need for expert annotations of regions of interest. We evaluate two prediction approaches: from the whole brain, and from an automatically defined tumor region. Finally, we employ interpretability methodologies as a qual作者: HPA533 時(shí)間: 2025-3-30 20:23
How Users Perceive Content-Based Image Retrieval for Identifying Skin Imagesption of confidence and trust. Our study with 16 novice users for a given set of annotated dermoscopy images indicates that, in general, CBIR enables novices to make a significantly more accurate classification on a new skin lesion image from four commonly-observed categories: Nevus, Seborrheic Kera作者: BYRE 時(shí)間: 2025-3-30 22:05 作者: 預(yù)防注射 時(shí)間: 2025-3-31 04:49
e oxygen species (ROS) that mediate programmed death of cells (apoptosis and necrosis) and organisms (phenoptosis). The latter process is considered as a key mechanism of aging which may be suppressed by mitochondria-targeted antioxidants.978-3-642-43582-9978-3-642-33430-6作者: 晚來的提名 時(shí)間: 2025-3-31 08:36
Clement Abi Nader,Nicholas Ayache,Philippe Robert,Marco Lorenzi,for the?Alzheimer’s?Disease?Neuroima the impression that the principles of biological control can be understood only after one has undergone a rather high-powered course in elec- tronic control theory. It often seems to be assumed that it is electronics which must do all the teaching while biology and medicine must do all the learning作者: defile 時(shí)間: 2025-3-31 09:28 作者: 緩和 時(shí)間: 2025-3-31 17:08
James R. Clough,Daniel R. Balfour,Claudia Prieto,Andrew J. Reader,Paul K. Marsden,Andrew P. Kingarch and treatment that will afford greater opportunities for a patient’s personalized cancer treatment. This was first envisioned in the 1987 initial edition of this textbook and is now a “new” and popular approach to cancer treatment. Some forms of cancer biotherapy use the strategy of tumor stabi作者: recession 時(shí)間: 2025-3-31 19:59 作者: amyloid 時(shí)間: 2025-4-1 00:58
