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Found 17 papers, 8 papers with code
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
To address this, we introduce PathMMU, the largest and highest-quality expert-validated pathology benchmark for Large Multimodal Models (LMMs).
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.
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis
Histopathology image analysis is the golden standard of clinical diagnosis for Cancers.
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.
Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting.
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.
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.
Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation.
Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images
We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches.
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning
In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains.
