Selecting only the relevant subsets from all gathered data has never been as challenging as it is in these times of big data and sensor fusion. Multiple complementary methods have emerged for the observation of similar phenomena; oftentimes, many of these techniques are superimposed in order to make the best possible decisions. A pathologist, for example, uses microscopic and spectroscopic techniques to discriminate between healthy and cancerous tissue. Especially in the field of spectroscopy in medicine, an immense number of frequencies are recorded and appropriately sized datasets are rarely acquired due to the time-intensive measurements and the lack of patients. In order to cope with the curse of dimensionality in machine learning, it is necessary to reduce the overhead from irrelevant or redundant features. In this article, we propose a feature selection callback algorithm (FeaSel-Net) that can be embedded in deep neural networks. It recursively prunes the input nodes after the optimizer in the neural network achieves satisfying results. We demonstrate the performance of the feature selection algorithm on different publicly available datasets and compare it to existing feature selection methods. Our algorithm combines the advantages of neural networks’ nonlinear learning ability and the embedding of the feature selection algorithm into the actual classifier optimization.

Detection and simultaneous monitoring of multiple trace gases is vital in scientific and industrial processes. Here, we use coherent control in quartz-enhanced photoacoustic spectroscopy (COCO-QEPAS) with an in situ learning method for rapid fingerprinting of trace gases to identify and monitor arbitrary gases at very low concentrations, without prior knowledge of gas composition. We validate this on various mixtures, including CH₄/C₂H₂/C₂H₄/C₂H₆/NO₂/NH₃. To this end, we demonstrate real-time analysis of mixtures containing up to four trace gases at ppm-level, monitoring changes in seconds using linear regression. The scalability of simultaneously distinguishable gases is straightforward. Furthermore, we expand fingerprinting to 10 ppm with a detection limit of 180 ppb CH₄, and apply empirical mode decomposition as an adaptive, data-driven filtering method to recover characteristic spectral features at the noise floor. For quantitative analysis in the ppb regime, we employ principal component regression as a calibration model that exploits correlations across the full spectrum. Consequently, our method offers significant potential for sensing applications where speed, accuracy, and simplicity are critical.

Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. WSIs require high magnifications to facilitate sub-cellular analysis. Precise annotations for patch- or even pixel-level classifications in the context of gigapixel WSIs are tedious to acquire and require domain experts. Coarse-grained labels, on the other hand, are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL). In our work, we propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL). We contribute to both the embedding and aggregation steps. Since all-purpose visual feature representations are not yet available, embedding models are currently limited in terms of generalizability. With our work, we explore the potential of dynamic meta-embedding based on cutting-edge self-supervised pre-trained models in the context of MIL. Moreover, we propose a new MIL architecture capable of combining MIL-attention with correlated self-attention. The Dual-Query Perceiver design of our approach allows us to leverage the concept of self-distillation and to combine the advantages of a small model in the context of a low data regime with the rich feature representation of a larger model. We demonstrate the superior performance of our approach on three histopathological datasets, where we show improvement of up to 10% over state-of-the-art approaches.