7 citations · 14 across the 4 of their papers we have counts for
4 papers
Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada +2
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used si…
Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology
Hiroki Tokunaga, Brian Kenji Iwana, Yuki Teramoto +2
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i.e., the coordinates of c…
Multi-Stage Pathological Image Classification using Semantic Segmentation
Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +5
Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigap…
Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology
Hiroki Tokunaga, Yuki Teramoto, Akihiko Yoshizawa +1
Automated digital histopathology image segmentation is an important task to help pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer subtypes, pa…