U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
[HTML][HTML] Deep learning to find colorectal polyps in colonoscopy: A systematic literature review
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly
increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and …
increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and …
Medical sam adapter: Adapting segment anything model for medical image segmentation
The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …
Medsegdiff: Medical image segmentation with diffusion probabilistic model
Abstract Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in
computer vision. Its image generation applications, such as Imagen, Latent Diffusion …
computer vision. Its image generation applications, such as Imagen, Latent Diffusion …
Medsegdiff-v2: Diffusion-based medical image segmentation with transformer
The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of
computer vision, thanks to its image generation applications, such as Imagen, Latent …
computer vision, thanks to its image generation applications, such as Imagen, Latent …
Learning calibrated medical image segmentation via multi-rater agreement modeling
In medical image analysis, it is typical to collect multiple annotations, each from a different
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …
An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization
Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and
caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening …
caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening …
EEG-based pathology detection for home health monitoring
An electroencephalogram (EEG)-based remote pathology detection system is proposed in
this study. The system uses a deep convolutional network consisting of 1D and 2D …
this study. The system uses a deep convolutional network consisting of 1D and 2D …
Medical sam 2: Segment medical images as video via segment anything model 2
J Zhu, Y Qi, J Wu - arxiv preprint arxiv:2408.00874, 2024 - arxiv.org
Medical image segmentation plays a pivotal role in clinical diagnostics and treatment
planning, yet existing models often face challenges in generalization and in handling both …
planning, yet existing models often face challenges in generalization and in handling both …
[HTML][HTML] End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images
The automated analysis of eye fundus images is crucial towards facilitating the screening
and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the …
and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the …