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Found 24 papers, 7 papers with code
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.
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.
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.
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.
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.
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.
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals.
Continual Learning Using Task Conditional Neural Networks
The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks.
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.
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis.
Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia.
Semi-supervised Federated Learning for Activity Recognition
In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data.
Verifying the Causes of Adversarial Examples
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence.
Continual Learning Using Multi-view Task Conditional Neural Networks
The changes in goals or data are referred to as new tasks in a continual learning model.
Continual Learning Using Bayesian Neural Networks
The catastrophic forgetting is an inevitable problem in continual learning models for dynamic environments.
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
The results show that our approach enables the model to continually learn and adapt to the new changes without forgetting the previously learned tasks.
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment.
