Search Results for author:
Found 21 papers, 9 papers with code
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.
B-Cos Aligned Transformers Learn Human-Interpretable Features
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology.
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.
HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation
To address these issues, we introduce hierarchical granularity discrimination, which exploits various levels of semantic information present in medical images.
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
Our approach combines a pre-trained network to extract deep features from the input image with iterative Richardson-Lucy deconvolution steps.
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.
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Our results demonstrate that LUCYD outperforms the state-of-the-art methods in both synthetic and real microscopy images, achieving superior performance in terms of image quality and generalisability.
Training Transitive and Commutative Multimodal Transformers with LoReTTa
We introduce LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy) to address this understudied problem.
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides.
Approaching Peak Ground Truth
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such.
MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain
Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data.
DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior
We present a model for non-blind image deconvolution that incorporates the classic iterative method into a deep learning application.
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.
Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction
Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities.
S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning
Evaluations of our framework on two public histopathological datasets show strong improvements in the case of sparse labels: for a H&E-stained colorectal cancer dataset, the accuracy increases by up to 9% compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia patient blood smears, the F1-score increases by up to 6%.
Multi-Modality Microscopy Image Style Transfer for Nuclei Segmentation
Annotating microscopy images for nuclei segmentation is laborious and time-consuming.
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.
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.
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.
Weakly-Supervised Structured Output Learning With Flexible and Latent Graphs Using High-Order Loss Functions
We introduce two new structured output models that use a latent graph, which is flexible in terms of the number of nodes and structure, where the training process minimises a high-order loss function using a weakly annotated training set.
