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Found 14 papers, 7 papers with code
The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques.
Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning
The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples.
Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning.
RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain.
DeeperHistReg: Robust Whole Slide Images Registration Framework
DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains.
SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography
SelfEEG is an open-source Python library developed to assist researchers in conducting Self-Supervised Learning (SSL) experiments on electroencephalography (EEG) data.
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion.
An overview of open source Deep Learning-based libraries for Neuroscience
The results show that, among a high number of available software tools, several libraries are standing out in terms of functionalities for neuroscience applications.
Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge
The importance of this task motivated researchers to organize the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022 conferences.
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
Despite several methods that were developed, stain colour heterogeneity is still an unsolved challenge that limits the development of CNNs that can generalize on data from several medical centers.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations
Global annotations refer to global findings in the high-resolution image and do not include information about the location of the regions of interest or the magnification levels used to identify a finding.
Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses
In this paper, we develop an automated way to detect stable fixations and show that gaze information is indeed helpful in predicting hand movements.
