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Found 13 papers, 6 papers with code
Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis
Using multi-modal data, whole slide images (WSIs) and clinical information, can improve the performance of deep learning models in the diagnosis of axillary lymph node metastasis.
WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
We benchmark our model on the largest subset of TCGA-PathoText.
Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
In this paper, we present a novel prompt-driven framework that consists of a nucleus prompter and SAM for automatic nucleus instance segmentation.
Test-Time Training for Semantic Segmentation with Output Contrastive Loss
Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge.
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps.
Masked conditional variational autoencoders for chromosome straightening
This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results.
Semi-supervised Cell Recognition under Point Supervision
Overall, we use the current models to generate pseudo labels for unlabeled images, which are in turn utilized to supervise the models training.
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
Secondly, by leveraging the collected data, we construct PathCLIP, a pathology-dedicated CLIP, to enhance PathAsst's capabilities in interpreting pathology images.
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification
Our framework is evaluated on five pathology WSI datasets on various WSI heads.
DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection
Unlike mainstream PCD methods that rely on intermediate density map representations, the Point-to-Point network (P2PNet) has recently emerged as an end-to-end solution for PCD, demonstrating impressive cell detection accuracy and efficiency.
Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images
To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation maps from the input images to avoid labeling costs and further produce pseudo masks for the downstream task.
End-to-end cell recognition by point annotation
Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification.
ChrSNet: Chromosome Straightening using Self-attention Guided Networks
Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities.
