Deep learning approaches and applications in toxicologic histopathology: Current status and future perspectives

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including ‘deep learning’, and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

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Deep learning in hepatocellular carcinoma: Current status and future perspectives

World Journal of Hepatology ◽

10.4254/wjh.v13.i12.2039 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2039-2051

Author(s):

Joseph C Ahn ◽

Touseef Ahmad Qureshi ◽

Amit G Singal ◽

Debiao Li ◽

Ju-Dong Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Current Status ◽

Future Perspectives

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Use of molecular technology in routine breast disease diagnosis and classification — Current status and future perspectives

European Journal of Cancer ◽

10.1016/s0959-8049(02)80124-1 ◽

2002 ◽

Vol 38 (11) ◽

pp. S48

Author(s):

J Costa

Keyword(s):

Breast Disease ◽

Disease Diagnosis ◽

Current Status ◽

Future Perspectives ◽

Diagnosis And Classification

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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