Special Section Guest Editorial: Medical Image Perception: Understanding How Radiologists Understand Images

Krupiński, Elizabeth A.

doi:10.1117/1.jmi.3.1.011001

Cited by 3 publications

(1 citation statement)

References 4 publications

(3 reference statements)

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“…With the pervasiveness of imaging in modern medicine, there has been significant attention and interest in the technological aspects of imaging operations ranging from hardware features to software functionalities. What is less appreciated is the perceptual act underlying the interpretation of these images (Krupinski, 2016;Manning et al, 2005;Wolfe, 2016). In order to impact patient care, an image must be perceived and interpreted (i.e., understood in the context of patient care) (Figure 1.1).…”

Section: Prominence Of Medical Image Perception In Medicinementioning

confidence: 99%

The Handbook of Medical Image Perception and Techniques

Samei¹,

Krupiński²

2018

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Section: Prominence Of Medical Image Perception In Medicinementioning

confidence: 99%

The Handbook of Medical Image Perception and Techniques

Samei¹,

Krupiński²

2018

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Medical Image Perception

Samei¹,

Krupiński²

2018

The Handbook of Medical Image Perception and Techniques

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Enhanced Medical Image Segmentation using Transfer Learning with Res101_UNet: Experimental Insights and Comparative Performance Analysis

Rolangi,

Bhaskari

2024

IJEAT

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Throughout the past few decades, artificial intelligence and machine learning have seen a lot of active research in areas such as computer vision, natural language processing, and speech processing. As a result, deep learning models became state-of-the-art for computer vision tasks such as object detection, classification, segmentation, and other allied tasks. Of course, the fruits of this research are extended to the design of robust and reliable digital health systems as well as other applications in the healthcare sector. Many clinical applications require the automatic segmentation of medical images. Recent deep learning-based approaches have demonstrated state-of-the-art performance in medical image segmentation tasks. In addition to their ability to automatically extract features and generalize over large amounts of data, transfer learning based deep learning models have proven to be handy for data scared areas like medical domains. In this research, we investigate and demonstrate the efficacy of a DCNN-based transfer learning model -Res101_Unet, which has been trained and/or fine-tuned to execute tumor tissue segmentation tasks in MRI, CT, PET, and X-RAY pictures of medical organ scans with little data. For our experimental study, we employed two image datasets: 'Liver Tumor' and 'Gland Colon Cancer', both obtained from the Kaggle portal. This experimental setup includes an Open-Source segmentation model API. Our findings indicate that domain similarity-based transfer learning can be used to data-scarce sectors. We achieved 98.47% accuracy and a IoU score of 0.9891 on Liver Tumor data and 0.6956 accuracy and a IoU score of 0.7043 on gland colon dataset.

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Special Section Guest Editorial: Medical Image Perception: Understanding How Radiologists Understand Images

Cited by 3 publications

References 4 publications

The Handbook of Medical Image Perception and Techniques

The Handbook of Medical Image Perception and Techniques

Medical Image Perception

Enhanced Medical Image Segmentation using Transfer Learning with Res101_UNet: Experimental Insights and Comparative Performance Analysis

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