[HTML][HTML] Deep learning for chest X-ray analysis: A survey
Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …
image analysis tasks. As the most commonly performed radiological exam, chest …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Automatic captioning for medical imaging (MIC): a rapid review of literature
Automatically understanding the content of medical images and delivering accurate
descriptions is an emerging field of artificial intelligence that combines skills in both …
descriptions is an emerging field of artificial intelligence that combines skills in both …
Follow my eye: Using gaze to supervise computer-aided diagnosis
When deep neural network (DNN) was first introduced to the medical image analysis
community, researchers were impressed by its performance. However, it is evident now that …
community, researchers were impressed by its performance. However, it is evident now that …
Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization
Features in images' backgrounds can spuriously correlate with the images' classes,
representing background bias. They can influence the classifier's decisions, causing …
representing background bias. They can influence the classifier's decisions, causing …
[HTML][HTML] Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19
Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia
have spread over the world, killing millions of people. Medical specialists have experienced …
have spread over the world, killing millions of people. Medical specialists have experienced …
CXray-EffDet: chest disease detection and classification from X-ray images using the EfficientDet model
The competence of machine learning approaches to carry out clinical expertise tasks has
recently gained a lot of attention, particularly in the field of medical-imaging examination …
recently gained a lot of attention, particularly in the field of medical-imaging examination …
Clip in medical imaging: A comprehensive survey
Contrastive Language-Image Pre-training (CLIP), a straightforward yet effective pre-training
paradigm, successfully introduces semantic-rich text supervision to vision models and has …
paradigm, successfully introduces semantic-rich text supervision to vision models and has …
Weak localization of radiographic manifestations in pulmonary tuberculosis from chest x-ray: A systematic review
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one
of the infectious diseases with the highest global mortalities. Chest radiography is a …
of the infectious diseases with the highest global mortalities. Chest radiography is a …
TS-DSANN: Texture and shape focused dual-stream attention neural network for benign-malignant diagnosis of thyroid nodules in ultrasound images
L Tang, C Tian, H Yang, Z Cui, Y Hui, K Xu… - Medical Image Analysis, 2023 - Elsevier
Recently, accurate diagnosis of thyroid nodules has played a critical role in precision
medicine and healthcare system management. Due to complicated changes in ultrasound …
medicine and healthcare system management. Due to complicated changes in ultrasound …