Applications of deep learning in fundus images: A review
The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …
importance. Due to its powerful performance, deep learning is becoming more and more …
Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the autoimplant 2021 cranial implant design challenge
Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects.
These implants are usually generated offline and may require days to weeks to be available …
These implants are usually generated offline and may require days to weeks to be available …
A foundation model for generalizable disease detection from retinal images
Medical artificial intelligence (AI) offers great potential for recognizing signs of health
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge
We described a challenge named" Diabetic Retinopathy (DR)—Grading and Image Quality
Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and …
Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and …
RTNet: relation transformer network for diabetic retinopathy multi-lesion segmentation
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting
ophthalmologists in diagnosis. Although many researches have been conducted on this …
ophthalmologists in diagnosis. Although many researches have been conducted on this …
Encoding structure-texture relation with p-net for anomaly detection in retinal images
Anomaly detection in retinal image refers to the identification of abnormality caused by
various retinal diseases/lesions, by only leveraging normal images in training phase …
various retinal diseases/lesions, by only leveraging normal images in training phase …
A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision
Foundation vision-language models are currently transforming computer vision, and are on
the rise in medical imaging fueled by their very promising generalization capabilities …
the rise in medical imaging fueled by their very promising generalization capabilities …
Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person
suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula …
suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula …
Adam challenge: Detecting age-related macular degeneration from fundus images
Age-related macular degeneration (AMD) is the leading cause of visual impairment among
elderly in the world. Early detection of AMD is of great importance, as the vision loss caused …
elderly in the world. Early detection of AMD is of great importance, as the vision loss caused …
The development of “automated visual evaluation” for cervical cancer screening: the promise and challenges in adapting deep‐learning for clinical testing
There is limited access to effective cervical cancer screening programs in many resource‐
limited settings, resulting in continued high cervical cancer burden. Human papillomavirus …
limited settings, resulting in continued high cervical cancer burden. Human papillomavirus …