Sameer Antani – författare
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3 produkter
3 produkter
Inbunden, Engelska, 2019
2 212 kr
Skickas inom 10-15 vardagar
Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community.The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
Del 13559 - Lecture Notes in Computer Science
Medical Image Learning with Limited and Noisy Data
First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Häftad, Engelska, 2022
558 kr
Skickas inom 10-15 vardagar
This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022.
Del 14307 - Lecture Notes in Computer Science
Medical Image Learning with Limited and Noisy Data
Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Häftad, Engelska, 2023
1 322 kr
Skickas inom 5-8 vardagar
This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.