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Found 12 papers, 5 papers with code
Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation
Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks.
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.
Exploiting 4D CT Perfusion for segmenting infarcted areas in patients with suspected acute ischemic stroke
CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions.
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.
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
Deep Learning for Predicting Metastasis on Melanoma WSIs
Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor.
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.
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.
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.
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.
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.
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.
