8 citations · 27 across the 22 of their papers we have counts for
6 papers · 1 filter
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
Takanori Asanomi, Shinnosuke Matsuo, Daiki Suehiro +1
Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are gi…
Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
Kazuya Nishimura, Ami Katanaya, Shinichiro Chuma +1
Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amoun…
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
Xiaoqing Liu, Kengo Araki, Shota Harada +7
The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the targe…
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation
Xiaoqing Liu, Kenji Ono, Ryoma Bise
The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also r…
Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection
Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe +1
Cell detection is an important task in biomedical research. Recently, deep learning methods have made it possible to improve the performance of cell detection. However, a detection…
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Shota Harada, Ryoma Bise, Kengo Araki +7
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a s…