Titlebook: Medical Image Understanding and Analysis; 24th Annual Conferen Bart?omiej W. Papie?,Ana I. L. Namburete,J. Alison Conference proceedings 20

mortality

書(shū)目名稱Medical Image Understanding and Analysis影響因子(影響力)




書(shū)目名稱Medical Image Understanding and Analysis影響因子(影響力)學(xué)科排名




書(shū)目名稱Medical Image Understanding and Analysis網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Medical Image Understanding and Analysis網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Medical Image Understanding and Analysis被引頻次




書(shū)目名稱Medical Image Understanding and Analysis被引頻次學(xué)科排名




書(shū)目名稱Medical Image Understanding and Analysis年度引用




書(shū)目名稱Medical Image Understanding and Analysis年度引用學(xué)科排名




書(shū)目名稱Medical Image Understanding and Analysis讀者反饋




書(shū)目名稱Medical Image Understanding and Analysis讀者反饋學(xué)科排名

BRIEF

攤位

neolith

CRAMP

方便

Segmenting Hepatocellular Carcinoma in Multi-phase CTapply input-level fusion for stacks of multi-phase data as channel input. Finally, we make use of a public single-phase CT liver tumour dataset for the pre-training of network parameters to improve the generalisation capabilities of our networks.

gospel

On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images presence of low contrast, when given suitable user input. In addition, we implement a deep learning algorithm based on this model, allowing for a supervised, semi-supervised or unsupervised approach, depending on data availability.

glucagon

Hypomania

Affable

Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentationxpect that the proposed training scheme would be applicable to any feedforward network and task. We show that the network can be used to harmonise two datasets and also show that the network is applicable in the common scenario of limited available training data, meaning that the network should be applicable for real-world segmentation problems.