Artificial intelligence (AI) has shown promising results for computational pathology tasks. However, one of the limitations in clinical practice is that these algorithms are optimised for the distribution represented by the training data. For out-of-distribution (OOD) data, they often deliver predictions with equal confidence, even though these often are incorrect. In the pursuit of OOD detection in digital pathology, this study evaluates the state-of-the-art (SOTA) in computational pathology OOD detection, based on diffusion probabilistic models, specifically by adapting the latent diffusion model (LDM) for this purpose (AnoLDM). We compare this against post-hoc methods based on the latent space of foundation models, which are SOTA in general computer vision research. The approaches are not only evaluated on data from the same medical centres as the training set, but also on several datasets with data distribution shifts. The results show that AnoLDM performs similarly well or better than diffusion model based approaches published in previous studies in computational pathology but with reduced computational costs. However, our optimal configuration of an approach based on foundation models (kang_residual) outperforms AnoLDM on OOD detection on data not experiencing any covariate shifts, with an AUROC of 96.17 versus 91.86. Interestingly, AnoLDM is more successful at handling the data distribution shifts investigated in this study. However, both AnoLDM and kang_residual suffer substantial loss in the performance under the data distribution shifts, hence future work should focus on improving the generalisation of OOD detection for computational pathology applications.

Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs remains a challenge. Conventional approaches rely on conditioning mechanisms, such as text prompts or semantic maps, which require extensively annotated datasets. In this preliminary work, we explore diffusion models conditioned on representations from a pre-trained self-supervised model. The self-conditioning mechanism not only improves the quality of unconditional image generation, but also provides a representation space that can be used to control the generation. We explore this conditioning space by identifying directions of variations, and demonstrate promising properties in terms of smoothness and disentanglement.

The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in ordinary differential equations is considered, with the typical application of finding weights of a neural ordinary differential equation (NODE) for a residual network with time continuous layers. The differential equation is treated as an abstract and isolated entity, termed a standalone NODE (sNODE), to facilitate for a wide range of applications. The proposed parameter reconstruction is performed by minimizing a cost functional covering a variety of loss functions and penalty terms. Regularization via penalty terms is incorporated to enhance ethical and trustworthy AI formulations. A nonlinear conjugate gradient mini-batch optimization scheme (NCG) is derived for the training having the benefit of including a sensitivity problem. The model (differential equation)-based approach is thus combined with a data-driven learning procedure. Mathematical properties are stated for the differential equation and the cost functional. The adjoint problem needed is derived together with the sensitivity problem. The sensitivity problem itself can estimate changes in the output under perturbation of the trained parameters. To preserve smoothness during the iterations, the Sobolev gradient is calculated and incorporated. Numerical results are included to validate the procedure for a NODE and synthetic datasets and compared with standard gradient approaches. For stability, using the sensitivity problem, a strategy for adversarial attacks is constructed, and it is shown that the given method with Sobolev gradients is more robust than standard approaches for parameter identification.

Traditional GAN (Generative Adversarial Network) architectures often reproduce biases present in their training data, leading to synthetic data that may unfairly impact certain subgroups. Past efforts to improve fairness in GANs usually target single demographic categories, like sex or race, but overlook intersectionality. Our approach addresses this gap by integrating an intersectionality framework with explainability techniques to identify and select problematic sensitive features. These insights are then used to develop intersectional fairness constraints integrated into the GAN training process. We aim to enhance fairness and maintain diverse subgroup representation by addressing intersections of multiple demographic attributes. Specifically, we adjusted the loss functions of two state-of-the-art GAN models for tabular data, including an intersectional demographic parity constraint. Our evaluations indicate that this approach significantly improves fairness in synthetically generated datasets. We compared the outcomes using Adult, and Diabetes datasets when considering the intersection of two sensitive features versus focusing on a single sensitive attribute, demonstrating the effectiveness of our method in capturing more complex biases.

Detecting novelties given unlabeled examples of normal data is a challenging task in machine learning, particularly when the novel and normal categories are semantically close. Large deep models pretrained on massive datasets can provide a rich representation space in which the simple k-nearest neighbor distance works as a novelty measure. However, as we show in this paper, the basic k-NN method might be insufficient in this context due to ignoring the 'local geometry' of the distribution over representations as well as the impact of irrelevant 'background features'. To address this, we propose a fully unsupervised novelty detection approach that integrates the flexibility of k-NN with a locally adapted scaling of dimensions based on the 'neighbors of nearest neighbor' and computing a 'likelihood ratio' in pretrained (self-supervised) representation spaces. Our experiments with image data show the advantage of this method when off-the-shelf vision transformers (e.g., pretrained by DINO) are used as the feature extractor without any fine-tuning.

Training a deep neural network is computationally expensive, but achieving the same network performance with less computation is possible if the training data is carefully chosen. However, selecting input samples during training is challenging as their true importance for the optimization is unknown. Furthermore, evaluation of the importance of individual samples must be computationally efficient and unbiased. In this paper, we present a new input data importance sampling strategy for reducing the training time of deep neural networks. We investigate different importance metrics that can be efficiently retrieved as they are available during training, i.e., the training loss and gradient norm. We found that choosing only samples with large loss or gradient norm, which are hard for the network to learn, is not optimal for the network performance. Instead, we introduce an importance sampling strategy that selects samples based on the cumulative distribution function of the loss and gradient norm, thereby making it more likely to choose hard samples while still including easy ones. The behavior of the proposed strategy is first analyzed on a synthetic dataset, and then evaluated in the application of classification of malignant cancer in digital pathology image patches. As pathology images contain many repetitive patterns, there could be significant gains in focusing on features that contribute stronger to the optimization. Finally, we show how the importance sampling process can be used to gain insights about the input data through visualization of samples that are found most or least useful for the training.

Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not clear how sensitive MIL is to clinically realistic domain shifts, i.e., differences in data distribution that could negatively affect performance, and if already existing metrics for detecting domain shifts work well with these algorithms. We trained an attention-based MIL algorithm to classify whether a whole-slide image of a lymph node contains breast tumour metastases. The algorithm was evaluated on data from a hospital in a different country and various subsets of this data that correspond to different levels of domain shift. Our contributions include showing that MIL for digital pathology is affected by clinically realistic differences in data, evaluating which features from a MIL model are most suitable for detecting changes in performance, and proposing an unsupervised metric named Fréchet Domain Distance (FDD) for quantification of domain shifts. Shift measure performance was evaluated through the mean Pearson correlation to change in classification performance, where FDD achieved 0.70 on 10-fold cross-validation models. The baselines included Deep ensemble, Difference of Confidence, and Representation shift which resulted in 0.45, -0.29, and 0.56 mean Pearson correlation, respectively. FDD could be a valuable tool for care providers and vendors who need to verify if a MIL system is likely to perform reliably when implemented at a new site, without requiring any additional annotations from pathologists.

As the problem of reconstructing high dynamic range (HDR) imagesfrom a single exposure has attracted much research effort, it isessential to provide a robust protocol and clear guidelines on howto evaluate and compare new methods. In this work, we comparedsix recent single image HDR reconstruction (SI-HDR) methodsin a subjective image quality experiment on an HDR display. Wefound that only two methods produced results that are, on average,more preferred than the unprocessed single exposure images. Whenthe same methods are evaluated using image quality metrics, astypically done in papers, the metric predictions correlate poorlywith subjective quality scores. The main reason is a significant toneand color difference between the reference and reconstructed HDRimages. To improve the predictions of image quality metrics, we propose correcting for the inaccuracies of the estimated cameraresponse curve before computing quality values. We further analyzethe sources of prediction noise when evaluating SI-HDR methodsand demonstrate that existing metrics can reliably predict onlylarge quality differences.

Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology.While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pin-pointing how the contrastive objective will behave differently due to the characteristics of histopathology data. Using SimCLR and H&E stained images as a representative setting for contrastive self-supervised learning in histopathology, we bring forward a number of considerations, such as view generation for the contrastive objectiveand hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications. Code and trained models are available at https://github.com/k-stacke/ssl-pathology.