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Found 10 papers, 1 papers with code
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation
However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure.
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches
Statistics of the envelope of the backscattered radiofrequency (RF) data can be utilized to estimate several QUS parameters.
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction
We use an attention-based approach that weighs the importance of different slide regions in producing the final classification results.
Phase Aberration Correction: A Deep Learning-Based Aberration to Aberration Approach
This dataset serves to mitigate the data scarcity problem in the development of deep learning-based techniques for phase aberration correction.
End-to-End Discriminative Deep Network for Liver Lesion Classification
In the present work we introduce an end-to-end deep learning approach to assist in the discrimination between liver metastases from colorectal cancer and benign cysts in abdominal CT images of the liver.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide.
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes.
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours.
