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Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining
Authors:
José Teixeira,
Pascal Klöckner,
Diana Montezuma,
Melis Erdal Cesur,
João Fraga,
Hugo M. Horlings,
Jaime S. Cardoso,
Sara P. Oliveira
Abstract:
In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly and labor-intensive technique, for which virtual staining, as an image-to-image translation task, offers a promising alternative. Although recent, this is an emerging field of research with 64% of published studies just…
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In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly and labor-intensive technique, for which virtual staining, as an image-to-image translation task, offers a promising alternative. Although recent, this is an emerging field of research with 64% of published studies just in 2024. Most studies use publicly available datasets of H&E-IHC pairs from consecutive tissue sections. Recognizing the training challenges, many authors develop complex virtual staining models based on conditional Generative Adversarial Networks, but ignore the impact of adversarial loss on the quality of virtual staining. Furthermore, overlooking the issues of model evaluation, they claim improved performance based on metrics such as SSIM and PSNR, which are not sufficiently robust to evaluate the quality of virtually stained images. In this paper, we developed CSSP2P GAN, which we demonstrate to achieve heightened pathological fidelity through a blind pathological expert evaluation. Furthermore, while iteratively developing our model, we study the impact of the adversarial loss and demonstrate its crucial role in the quality of virtually stained images. Finally, while comparing our model with reference works in the field, we underscore the limitations of the currently used evaluation metrics and demonstrate the superior performance of CSSP2P GAN.
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Submitted 24 November, 2025;
originally announced November 2025.
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An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
Authors:
Pedro C. Neto,
Diana Montezuma,
Sara P. Oliveira,
Domingos Oliveira,
João Fraga,
Ana Monteiro,
João Monteiro,
Liliana Ribeiro,
Sofia Gonçalves,
Stefan Reinhard,
Inti Zlobec,
Isabel M. Pinto,
Jaime S. Cardoso
Abstract:
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an app…
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Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
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Submitted 30 April, 2024; v1 submitted 6 January, 2023;
originally announced January 2023.
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Efficient and Modular Consensus-Free Reconfiguration for Fault-Tolerant Storage
Authors:
Eduardo Alchieri,
Alysson Bessani,
Fabiola Greve,
Joni Fraga
Abstract:
Quorum systems are useful tools for implementing consistent and available storage in the presence of failures. These systems usually comprise a static set of servers that provide a fault-tolerant read/write register accessed by a set of clients. We consider a dynamic variant of these systems and propose FreeStore, a set of fault-tolerant protocols that emulates a register in dynamic asynchronous s…
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Quorum systems are useful tools for implementing consistent and available storage in the presence of failures. These systems usually comprise a static set of servers that provide a fault-tolerant read/write register accessed by a set of clients. We consider a dynamic variant of these systems and propose FreeStore, a set of fault-tolerant protocols that emulates a register in dynamic asynchronous systems in which processes are able to join/leave the servers set during the execution. These protocols use a new abstraction called view generators, that captures the agreement requirements of reconfiguration and can be implemented in different system models with different properties. Particularly interesting, we present a reconfiguration protocol that is modular, efficient, consensus-free and loosely coupled with read/write protocols, improving the overall system performance.
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Submitted 18 July, 2016;
originally announced July 2016.