Titlebook: Hip Magnetic Resonance Imaging; Young-Jo Kim,Tallal Charles Mamisch Book 2014 Springer Science+Business Media, LLC 2014 Hip.MRI.Orthopedic

GLOSS

彎彎曲曲

Jeffrey J. Nepple MD,Young-Jo Kim MD, PhDng results to the allowed fuzziness level and the size of data history used. This study has shown that different datasets behave differently with changing these factors. Dataset behavior is correlated with the separation between clusters of the dataset.

殘忍

Kathleen L. Davenport MD,Peter J. Moley MD,Bryan T. Kelly MDf the broken region to the unbroken one. As a result, for every VOI, 29 morphometrical parameters were computed and used as initial features to the proposed DSS. The DSS comprises of two main modules: the feature selection module and the classifier. The feature selection module is used for reducing

Armory

al properties of the required input-output mapping using the minimum number of hidden nodes. Hidden nodes with least contribution to the error minimization at the output layer will be pruned. Experimental results show that the proposed pruning algorithm correctly prunes irrelevant hidden units.

外表讀作

Sarah D. Bixby MDencoder layers. We show that monolingual MahaBERT-based models provide rich representations as compared to sentence embeddings from multi-lingual counterparts. However, we observe that these embeddings are not generic enough and do not work well on out-of-domain social media datasets. We consider tw

Meditative

Bernd Bittersohl MD,Christoph Zilkens MD results with those of convolutional neural networks employed for the same tasks. Our findings support the use of Capsule Networks over Convolutional Neural Networks for Computer-Aided Diagnosis due to their superiority in performance but more importantly for their better interpretability and their

irreparable

Nancy A. Chauvin MD,Diego Jaramillo MD, MPHividuals who are distinguished from other pairs in the records by data-driven similarity measures between each individual in the transformed data. Such a design identifies the bias and mitigates it at the data preprocessing stage of the machine learning pipeline to ensure individual fairness. Our me

cogent

鎮(zhèn)痛劑

T. Charles Mamisch MDh incremental learning than through batch learning. As the size of training blocs decreases, the error rate acheived through incremental learning grows, but provides a more compact network using fewer training epochs. In the cases where the class distributions overlap, incremental learning shows sig

Counteract

ractical settings. Yet, especially for sensible domains such as FR we expect algorithms to work equally well for everyone, regardless of somebody’s age, gender, skin colour and/or origin. In this paper, we investigate a methodology to quantify the amount of bias in a trained Convolutional Neural Net