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ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration
This is complemented by a progressive data update strategy designed to ensure high-quality rendering for more complex maneuvers.
PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology
We conducted a qualitative assessment of the capabilities of the base model and the fine-tuned model in performing image captioning and classification tasks on the specific dataset.
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training
Deep Neural Networks (DNNs) are expected to provide explanation for users to understand their black-box predictions.
What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning
Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and model training efficacy.
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis.
