Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
[HTML][HTML] Review of multimodal machine learning approaches in healthcare
Abstract Machine learning methods in healthcare have traditionally focused on using data
from a single modality, limiting their ability to effectively replicate the clinical practice of …
from a single modality, limiting their ability to effectively replicate the clinical practice of …
Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism
Pulmonary Embolism (PE) represents a thrombus (“blood clot”), usually originating from a
lower extremity vein, that travels to the blood vessels in the lung, causing vascular …
lower extremity vein, that travels to the blood vessels in the lung, causing vascular …
A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound
Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …
representations for transfer learning, leveraging large amounts of unlabelled data. This …
[HTML][HTML] Learning anatomically consistent embedding for chest radiography
Self-supervised learning (SSL) approaches have recently shown substantial success in
learning visual representations from unannotated images. Compared with photographic …
learning visual representations from unannotated images. Compared with photographic …
ViTs as backbones: Leveraging vision transformers for feature extraction
Abstract The emergence of Vision Transformers (ViTs) has marked a significant shift in the
field of computer vision, presenting new methodologies that challenge traditional …
field of computer vision, presenting new methodologies that challenge traditional …
Toward Lightweight Diabetic Retinopathy Classification: A Knowledge Distillation Approach for Resource-Constrained Settings
Diabetic retinopathy (DR), a consequence of diabetes, is one of the prominent contributors
to blindness. Effective intervention necessitates accurate classification of DR; this is a need …
to blindness. Effective intervention necessitates accurate classification of DR; this is a need …
Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance
Deep learning nowadays offers expert-level and sometimes even super-expert-level
performance, but achieving such performance demands massive annotated data for training …
performance, but achieving such performance demands massive annotated data for training …
Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning
Y Shimada, T Ojima, Y Takaoka, A Sugano, Y Someya… - Surgery Today, 2024 - Springer
Purpose To develop deep learning models using thoracoscopic images to identify visceral
pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if …
pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if …
ThyFusion: A lightweight attribute enhancement module for thyroid nodule diagnosis using gradient and frequency-domain awareness
Accurate classification of thyroid nodules is a critical step in clinical diagnosis and plays a
crucial role in guiding subsequent treatment planning. However, current deep learning …
crucial role in guiding subsequent treatment planning. However, current deep learning …