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Found 12 papers, 4 papers with code
Ultrasound Image Enhancement using CycleGAN and Perceptual Loss
These images are compared with paired images acquired from high resolution devices to demonstrate the model's ability to generate realistic high-quality images across organ systems.
Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models
This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
Design considerations for a hierarchical semantic compositional framework for medical natural language understanding
Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods.
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time
We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0. 74, with a sensitivity of 0. 70 and a specificity of 0. 81 for classifying TSS < 4. 5 hours.
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression
We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Translating neural signals to text using a Brain-Computer Interface
To this end, we aim to create a BCI that decodes text directly from neural signals.
Translating neural signals to text using a Brain-Machine Interface
Brain-Computer Interfaces (BCI) help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech output by direct neural processing.
An attention-based multi-resolution model for prostate whole slide imageclassification and localization
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
