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Found 10 papers, 5 papers with code
DoNet: Deep De-overlapping Network for Cytology Instance Segmentation
Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei.
Deep Learning for Computational Cytology: A Survey
Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening.
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
The models were also compared to radiologists on a subset of the internal testing set (n=496).
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
Then, we inject a global classification head to the detection model and propose dual attention alignment to guide the global gradient to the local detection branch, which enables learning lesion detection from image-level annotations.
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing
The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data.
Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
The teacher's self-ensemble predictions from $K$-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student.
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity.
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction.
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment.
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Multi-tissue Nucleus Segmentation
on Kumar
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis.
