most citedA foundation for exact binarized morphological neural networks

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

eess.IV2024

3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation

Yi Gu, Yoshito Otake, Keisuke Uemura +6

Radiography is widely used in orthopedics for its affordability and low radiation exposure. 3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the p…

eess.IV2024

Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image

Yi Gu, Yoshito Otake, Keisuke Uemura +6

While most vision tasks are essentially visual in nature (for recognition), some important tasks, especially in the medical field, also require quantitative analysis (for quantific…

cs.LG20241 cited

A foundation for exact binarized morphological neural networks

Theodore Aouad, Hugues Talbot

Training and running deep neural networks (NNs) often demands a lot of computation and energy-intensive specialized hardware (e.g. GPU, TPU...). One way to reduce the computation a…

cs.CV2023

On the detection of Out-Of-Distribution samples in Multiple Instance Learning

Loïc Le Bescond, Maria Vakalopoulou, Stergios Christodoulidis +2

The deployment of machine learning solutions in real-world scenarios often involves addressing the challenge of out-of-distribution (OOD) detection. While significant efforts have…

eess.IV2023

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Yi Gu, Yoshito Otake, Keisuke Uemura +6

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantit…