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Found 24 papers, 12 papers with code
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
Our analysis shows that the local mask aligns with the attention patterns in the lower layers of the Transformer.
Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis
Though computer-aided automated diagnostic models can serve as strong complement for pathologists, their effectiveness is hampered by the paucity of extensive and detailed annotations, coupled with the limited interpretability and robustness.
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Slide Image (WSI) analysis.
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification
While existing methods to alleviate this issue introduce complex modules or processing steps, such as multiple-stage training and teacher-student distillation, this paper proposes a simple yet effective regularization: Attention Entropy Maximization (AEM).
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).
Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow
While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks.
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.
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.
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.
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning
In traffic signal control, flow-based (optimizing the overall flow) and pressure-based methods (equalizing and alleviating congestion) are commonly used but often considered separately.
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.
AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Data augmentation based methods construct pseudo-healthy images by "pasting" fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner.
Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
Patterns of near-crash events in a naturalistic driving dataset: applying rules mining
This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method.
Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification
In this paper, we propose a novel vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification.
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation
In this paper, to alleviate this issue, we introduce the semantic space of healthy anatomy in the process of modeling healthy-data distribution.
Hard Class Rectification for Domain Adaptation
Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
