133 research outputs found
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field
Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis
This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector
Towards Open Vocabulary Learning: A Survey
In the field of visual scene understanding, deep neural networks have made
impressive advancements in various core tasks like segmentation, tracking, and
detection. However, most approaches operate on the close-set assumption,
meaning that the model can only identify pre-defined categories that are
present in the training set. Recently, open vocabulary settings were proposed
due to the rapid progress of vision language pre-training. These new approaches
seek to locate and recognize categories beyond the annotated label space. The
open vocabulary approach is more general, practical, and effective compared to
weakly supervised and zero-shot settings. This paper provides a thorough review
of open vocabulary learning, summarizing and analyzing recent developments in
the field. In particular, we begin by comparing it to related concepts such as
zero-shot learning, open-set recognition, and out-of-distribution detection.
Then, we review several closely related tasks in the case of segmentation and
detection, including long-tail problems, few-shot, and zero-shot settings. For
the method survey, we first present the basic knowledge of detection and
segmentation in close-set as the preliminary knowledge. Next, we examine
various scenarios in which open vocabulary learning is used, identifying common
design elements and core ideas. Then, we compare the recent detection and
segmentation approaches in commonly used datasets and benchmarks. Finally, we
conclude with insights, issues, and discussions regarding future research
directions. To our knowledge, this is the first comprehensive literature review
of open vocabulary learning. We keep tracing related works at
https://github.com/jianzongwu/Awesome-Open-Vocabulary.Comment: Project page at https://github.com/jianzongwu/Awesome-Open-Vocabular
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