AIMC Topic: Image Processing, Computer-Assisted

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An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

IEEE transactions on biomedical circuits and systems
This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture ac...

Radiology and Enterprise Medical Imaging Extensions (REMIX).

Journal of digital imaging
Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner M...

Preprocessing Prediction of Advanced Algorithms for Medical Imaging.

Journal of digital imaging
Advanced medical imaging algorithms (such as bone removal, vessel segmentation, or a lung nodule detection) can provide extremely valuable information to the radiologists, but they might sometimes be very time consuming. Being able to run the algorit...

Parallel lensless compressive imaging via deep convolutional neural networks.

Optics express
We report a parallel lensless compressive imaging system, which enjoys real-time reconstruction using deep convolutional neural networks. A prototype composed of a low-cost LCD, 16 photo-diodes and isolation chambers, has been built. Each of these 16...

Muscle Segmentation for Orthopedic Interventions.

Advances in experimental medicine and biology
Skeletal muscle segmentation techniques can help orthopedic interventions in various scenes. In this chapter, we describe two methods of skeletal muscle segmentation on 3D CT images. The first method is based on a computational anatomical model, and ...

Fine-Tuning Deep Learning by Crowd Participation.

IEEE pulse
One of the major challenges currently facing researchers in applying deep learning (DL) models to medical image analysis is the limited amount of annotated data. Collecting such ground-truth annotations requires domain knowledge, cost, and time, maki...

AI and Clinicians: Not a Mutually Exclusive Zero-Sum Game.

IEEE pulse
Recent bold, eye-catching headline predictions made by nonradiologists, e.g., "in a few years, radiology will disappear" and "stop training radiologists now," are not only far from reality but also irresponsible and a disservice to the appropriate im...

Imaging Intelligence: AI Is Transforming Medical Imaging Across the Imaging Spectrum.

IEEE pulse
Artificial intelligence (AI) and machine learning (ML) have influenced medicine in myriad ways, and medical imaging is at the forefront of technological transformation. Recent advances in AI/ML fields have made an impact on imaging and image analysis...