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Found 38 papers, 13 papers with code
Frequency-Time Diffusion with Neural Cellular Automata
Denoising Diffusion Models (DDMs) have become the leading generative technique for synthesizing high-quality images but are often constrained by their UNet-based architectures that impose certain limitations.
Continual atlas-based segmentation of prostate MRI
Continual learning (CL) methods designed for natural image classification often fail to reach basic quality standards for medical image segmentation.
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models
We thoroughly evaluate the impact of the employed generative models on state-of-the-art neural networks in terms of accuracy, convergence speed and ensembling.
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology
Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation.
M3D-NCA: Robust 3D Segmentation with Built-in Quality Control
Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures.
Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models
Motivated by this, we analyse cataract surgery video data for the worst-performing phases of a pre-trained downstream tool classifier.
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
Federated Stain Normalization for Computational Pathology
As a solution, we propose BottleGAN, a generative model that can computationally align the staining styles of many laboratories and can be trained in a privacy-preserving manner to foster federated learning in computational pathology.
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisition quality and therefore subsequent analysis and final diagnosis.
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing.
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation.
FrOoDo: Framework for Out-of-Distribution Detection
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology.
Continual Hippocampus Segmentation with Transformers
Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.
Improving robustness and calibration in ensembles with diversity regularization
Calibration and uncertainty estimation are crucial topics in high-risk environments.
Disentanglement enables cross-domain Hippocampus Segmentation
This separation enables us to perform a domain transfer and thus convert data from new sources into the training domain.
Quality monitoring of federated Covid-19 lesion segmentation
It is, however, crucial to continuously monitor the performance of the model.
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
In this work, we evaluate OoD Generalization solutions for the problem of hippocampus segmentation in MR data using both fully- and semi-supervised training.
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability.
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic.
Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation
Conclusion: We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art.
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
A survey on shape-constraint deep learning for medical image segmentation
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmentation.
CycleGAN for Interpretable Online EMT Compensation
Domain-translated points are fine-tuned to reduce error in the bench domain.
Understanding Interpretability by generalized distillation in Supervised Classification
The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications.
Super-Selfish: Self-Supervised Learning on Images with PyTorch
Super-Selfish is an easy to use PyTorch framework for image-based self-supervised learning.
What is Wrong with Continual Learning in Medical Image Segmentation?
Continual learning protocols are attracting increasing attention from the medical imaging community.
Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation
Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems.
Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs
Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context.
M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans.
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical instruments.
GANs for Medical Image Analysis
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification.
Deep Spectral Correspondence for Matching Disparate Image Pairs
The proposed scheme reasons about correspondence between disparate images using high-level global shape cues derived from low-level local feature descriptors.
A Theory of Diagnostic Interpretation in Supervised Classification
We define the process of interpretation as a finite communication between a known model and a black-box model to optimally map the black box's decision process in the known model.
Exploring Adversarial Examples: Patterns of One-Pixel Attacks
Failure cases of black-box deep learning, e. g. adversarial examples, might have severe consequences in healthcare.
How Bad is Good enough: Noisy annotations for instrument pose estimation
In the application of surgical instrument pose estimation, where precision has a direct clinical impact on patient outcome, studying the effect of \emph{noisy annotations} on deep learning pose estimation techniques is of supreme importance.
i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery
Purpose: Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery.
Tool and Phase recognition using contextual CNN features
A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here.
