Robust deep learning-based multi-image super-resolution using inpainting

Yau, Henry; Du, Xian

doi:10.1117/1.jei.30.1.013005

Cited by 3 publications

(2 citation statements)

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“…One example of a self-supervised learning algorithm is image inpainting [3], where a model learns to reconstruct images from which parts have been blanked out. Such models have demonstrated a powerful ‘imagination’ for filling in missing data, restoring corrupted images, or achieving super-resolution [11]. Succeeding at such tasks implies that the trained embeddings must be capable of understanding, from surrounding context, what constitutes a plausible looking image; it is not such a stretch, then, to assume that such embeddings may be generically useful for a range of downstream tasks (image similarity, automated annotation, prediction, etc.…”

Section: Related Workmentioning

confidence: 99%

Image model embeddings for digital pathology and drug development via self-supervised learning

Baykaner

Bordeaux

et al. 2021

Preprint

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Whole slide images (WSIs) contain rich pathology information which can be used to diagnose cancer, characterize the tumour microenvironment (TME), assess patient prognosis, and provide insights into the likelihood of whether a patient may respond to a given treatment. However, since WSI availability is generally scarce during early stage clinical trials, the applicability of deep learning models to new and ongoing drug development in early stages is typically limited. WSIs available in public repositories, such as The Cancer Genome Atlas (TCGA), enable an unsupervised pretraining approach to help alleviate data scarcity. Pretrained models can also be utilised for a range of downstream applications such as automated annotation, quality control (QC), and similar image search. In this work we present DIME (Drug-development Image Model Embeddings), a pipeline for training image patch embeddings for WSIs via self-supervised learning. We compare inpainting and con- trastive learning approaches for embedding training in the DIME pipeline, and demonstrate state-of-the-art performance at image patch clustering. In addition, we show that the resultant embed- dings allow for training effective downstream patch classifiers with relatively few WSIs, and apply this to an AstraZeneca-sponsored phase III clinical trial. We also highlight the importance of effective colour normalisation for implementing histopathology analysis pipelines, regardless of the core learning algorithm. Finally, we show via subjective exploration of embedding spaces that the DIME pipeline clusters interesting histopathological artefacts, suggesting a possible role for the method in QC pipelines. By clustering image patches according to underlying morphopathologic features, DIME supports subsequent qualitative exploration by pathologists and has the potential to inform and expediate biomarker discovery and drug development.

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Section: Related Workmentioning

confidence: 99%

Image model embeddings for digital pathology and drug development via self-supervised learning

Baykaner

Bordeaux

et al. 2021

Preprint

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“…The quality of the image becomes particularly important because it affects the accurate interpretation and perception of the image. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred to a certain extent due to the acquisition of the imaging system, resulting in the degradation of quality of the images [ 1 , 2 , 3 , 4 ]. Therefore, it makes it hard for human observers to discriminate the fine details of the images such as edges and other features.…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Zheng

2021

Sensors

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Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.

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