Search Results for author:
Found 17 papers, 4 papers with code
Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients
Medical documentation, including discharge notes, is crucial for ensuring patient care quality, continuity, and effective medical communication.
InMD-X: Large Language Models for Internal Medicine Doctors
In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD).
NOTE: Notable generation Of patient Text summaries through Efficient approach based on direct preference optimization
The discharge summary is a one of critical documents in the patient journey, encompassing all events experienced during hospitalization, including multiple visits, medications, tests, surgery/procedures, and admissions/discharge.
Explaining How Deep Neural Networks Forget by Deep Visualization
Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life.
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding
SI considerably boosts accuracy and reduces the false prediction for trained models.
Applying Tensor Decomposition to image for Robustness against Adversarial Attack
However, it turns out a deep learning based model is highly vulnerable to some small perturbation called an adversarial attack.
Unbalanced GANs: Pre-training the Generator of Generative Adversarial Network using Variational Autoencoder
We propose Unbalanced GANs, which pre-trains the generator of the generative adversarial network (GAN) using variational autoencoder (VAE).
Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization
Interpreting the behaviors of Deep Neural Networks (usually considered as a black box) is critical especially when they are now being widely adopted over diverse aspects of human life.
ContCap: A scalable framework for continual image captioning
After proving forgetting in image captioning, we propose various techniques to overcome the forgetting dilemma by taking a simple fine-tuning schema as the baseline.
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation
Nowadays, Deep learning techniques show dramatic performance on computer vision area, and they even outperform human.
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes
Although there is a Ranking-CNN that takes into account the ordinal classes, it cannot reflect the inter-class relationship to the final prediction.
T-Net: Nested encoder-decoder architecture for the main vessel segmentation in coronary angiography
As a result, all features from the low-level to the high-level extracted from the encoder are delivered from the beginning of the decoder to predict a more accurate mask.
Tournament Based Ranking CNN for the Cataract grading
Because the case with a serious degree is not quite usual, there are imbalance in number of dataset between severe case and normal cases of diseases.
2sRanking-CNN: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input
In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma.
ECG arrhythmia classification using a 2-D convolutional neural network
In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition.
Automated diagnosis of pneumothorax using an ensemble of convolutional neural networks with multi-sized chest radiography images
Pneumothorax is a relatively common disease, but in some cases, it may be difficult to find with chest radiography.
Automated detection of vulnerable plaque in intravascular ultrasound images
The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule.
