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Found 14 papers, 8 papers with code
Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images
Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making.
Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
Here, we train a robust CNN for WBC classification by addressing cross-domain data imbalance and domain shifts.
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning
Querying the expert to annotate regions of interest in a WSI guides the formation of high-attention regions for MIL.
BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification
Recent MIL approaches produce highly informative bag level representations by utilizing the transformer architecture's ability to model the dependencies between instances.
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images
Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution.
Anomaly-aware multiple instance learning for rare anemia disorder classification
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations.
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears.
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy
Light-sheet fluorescence microscopy (LSFM) is a cutting-edge volumetric imaging technique that allows for three-dimensional imaging of mesoscopic samples with decoupled illumination and detection paths.
Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction
We propose to complement geometrical shape information by including multi-scale topological features, such as connected components, cycles, and voids, in the reconstruction loss.
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells.
Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations
Alternatively, color augmentation can be applied during training leading to a more robust model without the extra step of color normalization at test time.
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells.
InstantDL-An easy-to-use deep learning pipeline for image segmentation and classification
InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort.
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.
