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Found 25 papers, 13 papers with code
Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder
Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia.
Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
We test our approach on three datasets of white blood cell images and show that we achieve competitive performance compared to conventional methods.
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears.
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution process, with a hypervolume based likelihood promoting multi-objective descent.
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification
Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training.
Low-resource finetuning of foundation models beats state-of-the-art in histopathology
Recently, foundation models in computer vision showed that leveraging huge amounts of data through supervised or self-supervised learning improves feature quality and generalizability for a variety of tasks.
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences defocused image quality caused by light scattering when photons propagate through thick tissues.
A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification.
A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes
To that end, we train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups.
Topologically Regularized Multiple Instance Learning to Harness Data Scarcity
In biomedical data analysis, Multiple Instance Learning (MIL) models have emerged as a powerful tool to classify patients' microscopy samples.
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.
