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Found 6 papers, 2 papers with code
Functional Parcellation of fMRI data using multistage k-means clustering
Conclusion: The multistage k-means approach can provide functional parcellations of the brain using resting state fMRI data.
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Histopathological analysis is the present gold standard for precancerous lesion diagnosis.
Feature based Sequential Classifier with Attention Mechanism
Cervical cancer is one of the deadliest cancers affecting women globally.
Selective Synthetic Augmentation with Quality Assurance
To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.
Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data.
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
