Opportunities and challenges for machine learning in rare diseases
Rare diseases (RDs) are complicated health conditions that are difficult to be managed at
several levels. The scarcity of available data chiefly determines an intricate scenario even …
several levels. The scarcity of available data chiefly determines an intricate scenario even …
Gaitforemer: Self-supervised pre-training of transformers via human motion forecasting for few-shot gait impairment severity estimation
Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-
related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture …
related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture …
SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images
Background Deep learning has revolutionized digital pathology, allowing automatic analysis
of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs …
of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs …
Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a sco** review
Background Early detection and diagnosis of cancer are vital to improving outcomes for
patients. Artificial intelligence (AI) models have shown promise in the early detection and …
patients. Artificial intelligence (AI) models have shown promise in the early detection and …
MFNet: Meta‐learning based on frequency‐space mix for MRI segmentation in nasopharyngeal carcinoma
Y Li, Q Chen, H Li, S Wang, N Chen… - Journal of Cellular …, 2024 - Wiley Online Library
Deep learning techniques have been applied to medical image segmentation and
demonstrated expert‐level performance. Due to the poor generalization abilities of the …
demonstrated expert‐level performance. Due to the poor generalization abilities of the …
[HTML][HTML] Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks
The advent of multiple neuroimaging methodologies has greatly aided in the
conceptualization of large-scale functional brain networks in the field of cognitive …
conceptualization of large-scale functional brain networks in the field of cognitive …
[HTML][HTML] Bidirectional Copy–Paste Mamba for Enhanced Semi-Supervised Segmentation of Transvaginal Uterine Ultrasound Images
B Peng, Y Liu, W Wang, Q Zhou, L Fang, X Zhu - Diagnostics, 2024 - mdpi.com
Automated perimetrium segmentation of transvaginal ultrasound images is an important
process for computer-aided diagnosis of uterine diseases. However, ultrasound images …
process for computer-aided diagnosis of uterine diseases. However, ultrasound images …
MUE-CoT: multi-scale uncertainty entropy-aware co-training framework for left atrial segmentation
D Hao, H Li, Y Zhang, Q Zhang - Physics in Medicine & Biology, 2023 - iopscience.iop.org
Objective. Accurate left atrial segmentation is the basis of the recognition and clinical
analysis of atrial fibrillation. Supervised learning has achieved some competitive …
analysis of atrial fibrillation. Supervised learning has achieved some competitive …
A hybrid residual attention convolutional neural network for compressed sensing magnetic resonance image reconstruction
We propose a dual-domain deep learning technique for accelerating compressed sensing
magnetic resonance image reconstruction. An advanced convolutional neural network with …
magnetic resonance image reconstruction. An advanced convolutional neural network with …
[HTML][HTML] Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning
Abstract Background/Objectives: Breast cancer is the most common cancer in women
worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a …
worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a …