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Found 9 papers, 2 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).
Benchmarking PathCLIP for Pathology Image Analysis
Additionally, we assess the robustness of PathCLIP in the task of image-image retrieval, revealing that PathCLIP performs less effectively than PLIP on Osteosarcoma but performs better on WSSS4LUAD under diverse corruptions.
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.
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.
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.
ChrSNet: Chromosome Straightening using Self-attention Guided Networks
Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities.
Dispensed Transformer Network for Unsupervised Domain Adaptation
To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes.
