[HTML][HTML] Glaucoma diagnosis in the era of deep learning: A survey
Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic
challenges due to its reliance on subjective evaluation. Recent advances in computer vision …
challenges due to its reliance on subjective evaluation. Recent advances in computer vision …
Cost-sensitive learning for imbalanced medical data: a review
Abstract Integrating Machine Learning (ML) in medicine has unlocked many opportunities to
harness complex medical data, enhancing patient outcomes and advancing the field …
harness complex medical data, enhancing patient outcomes and advancing the field …
On supervised class-imbalanced learning: An updated perspective and some key challenges
S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
An interpretable and accurate deep-learning diagnosis framework modelled with fully and semi-supervised reciprocal learning
The deployment of automated deep-learning classifiers in clinical practice has the potential
to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance …
to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance …
Interpretable machine learning‐assisted high‐throughput screening for understanding NRR electrocatalyst performance modulation between active center and C‐N …
J Sun, A Chen, J Guan, Y Han, Y Liu… - Energy & …, 2024 - Wiley Online Library
Understanding the correlation between the fundamental descriptors and catalytic
performance is meaningful to guide the design of high‐performance electrochemical …
performance is meaningful to guide the design of high‐performance electrochemical …
Curriculum label distribution learning for imbalanced medical image segmentation
Label distribution learning (LDL) has the potential to resolve boundary ambiguity in
semantic segmentation tasks. However, existing LDL-based segmentation methods suffer …
semantic segmentation tasks. However, existing LDL-based segmentation methods suffer …
RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps
Optical coherence tomography imaging provides a crucial clinical measurement for
diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer …
diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer …
Adversarial learning-based multi-level dense-transmission knowledge distillation for AP-ROP detection
H Xie, Y Liu, H Lei, T Song, G Yue, Y Du, T Wang… - Medical Image …, 2023 - Elsevier
Abstract The Aggressive Posterior Retinopathy of Prematurity (AP-ROP) is the major cause
of blindness for premature infants. The automatic diagnosis method has become an …
of blindness for premature infants. The automatic diagnosis method has become an …
Hierarchical-instance contrastive learning for minority detection on imbalanced medical datasets
Deep learning methods are often hampered by issues such as data imbalance and data-
hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in …
hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in …
Deep learning and computer vision for glaucoma detection: A review
Glaucoma is the leading cause of irreversible blindness worldwide and poses significant
diagnostic challenges due to its reliance on subjective evaluation. However, recent …
diagnostic challenges due to its reliance on subjective evaluation. However, recent …