10 research outputs found
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
Multiple Sclerosis (MS) is a severe neurological disease characterized by
inflammatory lesions in the central nervous system. Hence, predicting
inflammatory disease activity is crucial for disease assessment and treatment.
However, MS lesions can occur throughout the brain and vary in shape, size and
total count among patients. The high variance in lesion load and locations
makes it challenging for machine learning methods to learn a globally effective
representation of whole-brain MRI scans to assess and predict disease.
Technically it is non-trivial to incorporate essential biomarkers such as
lesion load or spatial proximity. Our work represents the first attempt to
utilize graph neural networks (GNN) to aggregate these biomarkers for a novel
global representation. We propose a two-stage MS inflammatory disease activity
prediction approach. First, a 3D segmentation network detects lesions, and a
self-supervised algorithm extracts their image features. Second, the detected
lesions are used to build a patient graph. The lesions act as nodes in the
graph and are initialized with image features extracted in the first stage.
Finally, the lesions are connected based on their spatial proximity and the
inflammatory disease activity prediction is formulated as a graph
classification task. Furthermore, we propose a self-pruning strategy to
auto-select the most critical lesions for prediction. Our proposed method
outperforms the existing baseline by a large margin (AUCs of 0.67 vs. 0.61 and
0.66 vs. 0.60 for one-year and two-year inflammatory disease activity,
respectively). Finally, our proposed method enjoys inherent explainability by
assigning an importance score to each lesion for the overall prediction. Code
is available at https://github.com/chinmay5/ms_ida.gi
INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses
In view of the recent paradigm shift in deep AI based image processing
methods, medical image processing has advanced considerably. In this study, we
propose a novel deep neural network (DNN), entitled InceptNet, in the scope of
medical image processing, for early disease detection and segmentation of
medical images in order to enhance precision and performance. We also
investigate the interaction of users with the InceptNet application to present
a comprehensive application including the background processes, and foreground
interactions with users. Fast InceptNet is shaped by the prominent Unet
architecture, and it seizes the power of an Inception module to be fast and
cost effective while aiming to approximate an optimal local sparse structure.
Adding Inception modules with various parallel kernel sizes can improve the
network's ability to capture the variations in the scaled regions of interest.
To experiment, the model is tested on four benchmark datasets, including retina
blood vessel segmentation, lung nodule segmentation, skin lesion segmentation,
and breast cancer cell detection. The improvement was more significant on
images with small scale structures. The proposed method improved the accuracy
from 0.9531, 0.8900, 0.9872, and 0.9881 to 0.9555, 0.9510, 0.9945, and 0.9945
on the mentioned datasets, respectively, which show outperforming of the
proposed method over the previous works. Furthermore, by exploring the
procedure from start to end, individuals who have utilized a trial edition of
InceptNet, in the form of a complete application, are presented with thirteen
multiple choice questions in order to assess the proposed method. The outcomes
are evaluated through the means of Human Computer Interaction
A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond
Over the past decade, deep learning technologies have greatly advanced the
field of medical image registration. The initial developments, such as
ResNet-based and U-Net-based networks, laid the groundwork for deep
learning-driven image registration. Subsequent progress has been made in
various aspects of deep learning-based registration, including similarity
measures, deformation regularizations, and uncertainty estimation. These
advancements have not only enriched the field of deformable image registration
but have also facilitated its application in a wide range of tasks, including
atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D
registration. In this paper, we present a comprehensive overview of the most
recent advancements in deep learning-based image registration. We begin with a
concise introduction to the core concepts of deep learning-based image
registration. Then, we delve into innovative network architectures, loss
functions specific to registration, and methods for estimating registration
uncertainty. Additionally, this paper explores appropriate evaluation metrics
for assessing the performance of deep learning models in registration tasks.
Finally, we highlight the practical applications of these novel techniques in
medical imaging and discuss the future prospects of deep learning-based image
registration