Search Results for author:
Found 23 papers, 11 papers with code
Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection
Second, we propose a pairwise detection method, which uses the information of detection results at the previous frame for detecting cells at the current frame.
Counting Network for Learning from Majority Label
Existing MIL methods are unsuitable for LML due to aggregating confidences, which may lead to inconsistency between the bag-level label and the label obtained by counting the number of instances for each class.
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary experiments; that the instance-level classification accuracy improves as the number of labeled bags increases even though the total number of instances is fixed.
Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
First, we generate an image pair not containing mitosis events by frame-order flipping.
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation
The development of medical image segmentation using deep learning can significantly support doctors' diagnoses.
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
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 target domain (from different hospitals) due to the different image features.
Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection
In particular, cells are cultured under different conditions depending on the purpose of the research.
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
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 small number of labeled samples from the target domain.
Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization
This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags.
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data
This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation.
Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties.
Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand.
Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data.
Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency
This annotation is a time-consuming and labor-intensive task.
Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
We propose an unsupervised domain adaptation method for cell detection using the pseudo-cell-position heatmap, where a cell centroid becomes a peak with a Gaussian distribution in the map.
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation
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 cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology
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 cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN
In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN.
MPM: Joint Representation of Motion and Position Map for Cell Tracking
Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association).
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data.
Cell Segmentation
Cultural Vocal Bursts Intensity Prediction
+2
Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology
A key assumption is that the importance of the magnifications depends on the characteristics of the input image, such as cancer subtypes.
Virtual Blood Vessels in Complex Background using Stereo X-ray Images
We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat.
Wetness and Color From a Single Multispectral Image
We derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface.
