Predicting survival rates based on multi-gigapixel histopathology images is one of the most challenging tasks in digital pathology. Due to the computational complexities, Multiple Instance Learning (MIL) has become th...
ISBN:
(纸本)9798350307184
Predicting survival rates based on multi-gigapixel histopathology images is one of the most challenging tasks in digital pathology. Due to the computational complexities, Multiple Instance Learning (MIL) has become the conventional approach for this process as it breaks the image into smaller patches. However, this technique fails to account for the individual cells present in each patch, while they are the fundamental part of the tissue. In this work, we developed a novel dynamic and hierarchical point-cloud-based method (CO-PILOT) for the processing of cellular graphs extracted from routine histopathology images. By using bottom-up information propagation and top-down conditional attention, our model gains access to an adaptive focus across different levels of tissue hierarchy. Through comprehensive experiments, we demonstrate that our model can outperform all the state-of-the-art methods in survival prediction, including the hierarchical vision Transformer ( ViT), across three datasets and four metrics with only half of the parameters of the closest baseline. Importantly, our model is able to stratify the patients into different risk cohorts with statistically different outcomes across three large datasets, a task that was previously achievable only using genomic information. Furthermore, we publish a large dataset containing 873 cellular graphs from 188 patients, along with their survival information, making it one of the largest publicly available datasets in this context.
image Signal Processors (ISPs) play important roles in image recognition tasks as well as in the perceptual quality of captured images. In most cases, experts make a lot of effort to manually tune many parameters of I...
ISBN:
(纸本)9798350307184
image Signal Processors (ISPs) play important roles in image recognition tasks as well as in the perceptual quality of captured images. In most cases, experts make a lot of effort to manually tune many parameters of ISPs, but the parameters are sub-optimal. In the literature, two types of techniques have been actively studied: a machine learning-based parameter tuning technique and a DNN-based ISP technique. The former is lightweight but lacks expressive power. The latter has expressive power, but the computational cost is too heavy on edge devices. To solve these problems, we propose "DynamicISP," which consists of multiple classical ISP functions and dynamically controls the parameters of each frame according to the recognition result of the previous frame. We show our method successfully controls the parameters of multiple ISP functions and achieves state-of-the-art accuracy with low computational cost in single and multi-category object detection tasks.
Large-scale text-to-image models pre-trained on massive text-image pairs show excellent performance in image synthesis recently. However, image can provide more intuitive visual concepts than plain text. People may as...
ISBN:
(纸本)9798350307184
Large-scale text-to-image models pre-trained on massive text-image pairs show excellent performance in image synthesis recently. However, image can provide more intuitive visual concepts than plain text. People may ask: how can we integrate the desired visual concept into an existing image, such as our portrait? Current methods are inadequate in meeting this demand as they lack the ability to preserve content or translate visual concepts effectively. Inspired by this, we propose a novel framework named visual concept translator (VCT) with the ability to preserve content in the source image and translate the visual concepts guided by a single reference image. The proposed VCT contains a content-concept inversion (CCI) process to ex*The first two authors contributed equally to this work. tract contents and concepts, and a content-concept fusion (CCF) process to gather the extracted information to obtain the target image. Given only one reference image, the proposed VCT can complete a wide range of general imageto-image translation tasks with excellent results. Extensive experiments are conducted to prove the superiority and effectiveness of the proposed methods. Codes are available at https://***/CrystalNeuro/visual-concept-translator.
image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approach...
ISBN:
(纸本)9798350307443
image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approaches do not translate to datasets where additional information is provided as discrete classes. We introduce and implement a model which combines image-to-image and class-guided denoising diffusion probabilistic models. We train our model on a real-world dataset of microscopy images used for drug discovery, with and without incorporating metadata labels. By exploring the properties of image-to-image diffusion with relevant labels, we show that class-guided image-to-image diffusion can improve the meaningful content of the reconstructed images and outperform the unguided model in useful downstream tasks.
In this research, we provide a method for encrypting digital images that is based on DNA block encoding and multi-chaotic systems. First, create a scrambling matrix using the one-dimensional logistic chaotic map, then...
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Due to the limitations of traditional video imageprocessing methods, such as the inability to accurately obtain the location information of moving targets, and the inability to meet practical application requirements...
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image Coding for Machine (ICM) aims to compress an image so that the reconstructed one can meet the requirements of both human vision and machine vision. Existing methods apply the constraint from the downstream model...
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ISBN:
(纸本)9798350344868;9798350344851
image Coding for Machine (ICM) aims to compress an image so that the reconstructed one can meet the requirements of both human vision and machine vision. Existing methods apply the constraint from the downstream models to improve machine analytics performance while compromising the visual quality. This paper proposes a novel adversarially augmented adaptation route that achieves a better trade-off between the utility of the human and machine perspectives by making slight changes to the image manifold. In detail, a targeted adversarial attack is employed to generate subtle image perturbations that are nearly imperceptible to humans but significantly improve machine analytic performance. These perturbed images would be subsequently employed as ground truth to guide training/fine-tuning of an end-to-end image compression network. Note that, our method is a plug-and-play framework that does not rely on any change in existing architecture or loss functions. Extensive experimental results demonstrate the superiority of the proposed scheme over conventional ICM frameworks and the effectiveness of our design.
image denoising is a representative image restoration task in computervision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful ima...
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ISBN:
(纸本)9781728198354
image denoising is a representative image restoration task in computervision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful image denoising from only a noisy image by inductive bias of convolutional neural network architectures without any pre-training. The major challenge of DIP based image denoising is that DIP would completely recover the original noisy image unless applying early stopping. For early stopping without a ground-truth clean image, we propose to monitor JPEG file size of the recovered image during optimization as a proxy metric of noise levels in the recovered image. Our experiments show that the compressed image file size works as an effective metric for early stopping.
Fine-grained image classification aims to accurately categorize subclasses within a particular category. Due to the small inter-class differences and large intra-class variations, fine-grained image classification has...
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Single-photon sensors measure light signals at the finest possible resolution - individual photons. These sensors introduce two major challenges in the form of strong Poisson noise and extremely large data acquisition...
ISBN:
(纸本)9798350307184
Single-photon sensors measure light signals at the finest possible resolution - individual photons. These sensors introduce two major challenges in the form of strong Poisson noise and extremely large data acquisition rates, which are also inherited by downstream computervision tasks. Previous work has largely focused on solving the image reconstruction problem first and then using off-the-shelf methods for downstream tasks, but the most general solutions that account for motion are costly and not scalable to large data volumes produced by single-photon sensors. This work forgoes the image reconstruction problem. Instead, we demonstrate computationally light-weight phasebased algorithms for the tasks of edge detection and motion estimation. These methods directly process the raw singlephoton data as a 3D volume with a bank of velocity-tuned filters, achieving speed-ups of more than two orders of magnitude compared to explicit reconstruction-based methods. Project webpage: https://***/ project/eulerian-single-photon-vision/
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