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Found 9 papers, 1 papers with code
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma
Results: The model uses an image as input and produces a 2-dimensional black and white output of prediction of the same dimension; the areas determined to be basal cell carcinoma were displayed with white color, in a black background.
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today.
Assessment of Amazon Comprehend Medical: Medication Information Extraction
To further establish the generalizability of its medication extraction performance, a set of random internal clinical text notes from NYU Langone Medical Center were also included in this work.
Intracranial Hemorrhage Segmentation Using Deep Convolutional Model
The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions.
Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina
We report, to our knowledge, the first end-to-end application of Generative Adversarial Networks (GANs) towards the synthesis of Optical Coherence Tomography (OCT) images of the retina.
Wearable-based Mediation State Detection in Individuals with Parkinson's Disease
There is a need to a technology-based system that can provide objective measures about the duration in different medication states that can be used by the treating physician to successfully adjust the therapy.
Geared Rotationally Identical and Invariant Convolutional Neural Network Systems
Theorems and techniques to form different types of transformationally invariant processing and to produce the same output quantitatively based on either transformationally invariant operators or symmetric operations have recently been introduced by the authors.
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions
The main challenge is to automatically estimate the desired plane of symmetry within the patient's pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey's biweight robust estimator, relying on the partial symmetry of the anatomy.
