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Found 16 papers, 9 papers with code
Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images
Our method detects lesions with high accuracy and localizes them on a WSI to identify potential regions of interest for pathologists.
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate.
Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke.
CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging
However, there is no consensus in terms of which thresholds to use, or how to combine the information from the parametric maps, and the presented methods all have limitations in terms of both accuracy and reproducibility.
Nested Multiple Instance Learning with Attention Mechanisms
Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications this is a major drawback.
CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke
CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions.
Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation
Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks.
Ranked #3 on
Artifact Detection
on HistoArtifacts
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
We developed DL pipelines using two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs).
Ranked #1 on
Artifact Detection
on HistoArtifacts
(1:1 Accuracy metric)
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
We achieved 0. 996 and 0. 938 F1 scores for blur and folded tissue detection on unseen data, respectively.
Ranked #1 on
Artifact Detection
on HistoArtifacts
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks.
Deep Learning for Predicting Metastasis on Melanoma WSIs
Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor.
Object Detection During Newborn Resuscitation Activities
Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities
Activity Recognition From Newborn Resuscitation Videos
An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes.
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading
In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128x128x3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task.
Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education
The advancement of biomedical research heavily relies on access to large amounts of medical data.
A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics
Melanoma is a type of cancer that begins in the cells controlling the pigment of the skin, and it is often referred to as the most dangerous skin cancer.
