Publications by authors named "D Samaras"

Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides. Traditionally, MIL interpretability is limited to identifying salient regions deemed pertinent for downstream tasks, offering little insight to the end-user (pathologist) regarding the rationale behind these selections. To address this, we propose Self-Interpretable MIL (SI-MIL), a method intrinsically designed for interpretability from the very outset.

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Article Synopsis
  • Diffusion models can improve image generation in specialized fields like histopathology and satellite imagery by utilizing self-supervised learning (SSL) embeddings as stand-ins for human labels, which are hard to obtain.
  • This new method allows for high-quality images to be created from these embeddings, and it can even generate larger images by combining smaller patches while maintaining their spatial consistency.
  • The approach enhances classifier performance on both small patch-level and larger scale classification tasks and shows strong adaptability, successfully working with unseen datasets and different input sources, including text descriptions for image synthesis.
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Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper, we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology, we propose to adopt novel learning method that can fully exploit unlabeled data.

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Satellite-based remote sensing and uncrewed aerial imagery play increasingly important roles in the mapping of wildlife populations and wildlife habitat, but the availability of imagery has been limited in remote areas. At the same time, ecotourism is a rapidly growing industry and can yield a vast catalog of photographs that could be harnessed for monitoring purposes, but the inherently ad-hoc and unstructured nature of these images make them difficult to use. To help address this, a subfield of computer vision known as phototourism has been developed to leverage a diverse collection of unstructured photographs to reconstruct a georeferenced three-dimensional scene capturing the environment at that location.

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Steatotic liver disease has been shown to associate with cardiovascular disease independently of other risk factors. Lipoproteins have been shown to mediate some of this relationship but there remains unexplained variance. Here we investigate the plasma lipidomic changes associated with liver steatosis and the mediating effect of these lipids on coronary artery disease (CAD).

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