Enhancing gland segmentation in colon histology images using an instance-aware diffusion model

Sun, Mengxue; Wang, Jiale; Gong, Qingtao; Huang, Wenhui

doi:10.1016/j.compbiomed.2023.107527

Cited by 15 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, some publicly available medical image datasets [72] are described in Table 7. The Cancer Genome Atlas -Lung (TCGA-Lung) [89] Breast Digital Database for Screening Mammography (DDSM) [72] CBIS-DDSM (Curated Breast Imaging Subset of DDSM) [72] Breast ultrasound image (BUSI and BUS) [84] INbreast Dataset [90] Breast Cancer Semantic Segmentation (BCSS) [91] Colon 2015 MICCAI Gland Segmentation challenge (GlaS) [92] Colorectal Adenocarcinoma Glands (CRAG) [92] RINGS [92] Skin DermNet [72] International Skin Imaging Collaboration (ISIC) Archive [93] Diverse Dermatology Images (DDI) […”

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Sarkar,

Lee,

Sahoo

2024

Electronics

View full text Add to dashboard Cite

Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

“…IAFMSMB Net [92] Utilized Instance Aware Filters and a multi-scale Mask Branch to generate a global mask and employed Conditional Encoding to enhance intermediate features.…”

Section: Model Name Brief Descriptions Aspects To Addressmentioning

confidence: 99%