Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare.
First, modifying only the class token of the text prompt results in a greater impact on the Class Activation Map (CAM), compared to arguably more complex strategies that optimize the context.
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain.
Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health.
In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction.
We propose a multiple acquisition context based network, called MAC-ReconNet for MRI reconstruction, flexible to multiple acquisition contexts and generalizable to unseen contexts for applicability in real scenarios.
A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper.
Furthermore, the model was also evaluated on three other databases.
In our work, we propose a knowledge distillation (KD) framework for the image to image problems in the MRI workflow in order to develop compact, low-parameter models without a significant drop in performance.
However, these traditional methods require expert knowledge and are unable to model a wide range of arrhythmia.
Among them, U-Net has shown to be the baseline architecture for MR image reconstruction.
Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class imbalance because of its cross entropy (CE) objective function.
no code implementations • 8 Oct 2019 • José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel van Keer, Deepti. R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, Joonho Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton Van Den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance.
For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications.
We further evaluate the signal denoising using Mean Square Error(MSE) and Cross Correlation between model predictions and ground truth.
Ranked #1 on ECG Denoising on UnoViS_auto2012
no code implementations • 12 Feb 2019 • Vignesh Ravichandran, Balamurali Murugesan, Vaishali Balakarthikeyan, Sharath M. Shankaranarayana, Keerthi Ram, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam
Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring.
We also propose a new joint loss function for the proposed architecture.
We modify the decoder part of the FCN to exploit class information and the structural information as well.