Active Infrared thermography (AIRT) is a widely adopted non-destructive testing (NDT) technique for detecting subsurface anomalies in industrial components. Due to the high dimensionality of AIRT data, current approaches employ non-linear autoencoders (AEs) for dimensionality reduction. However, the latent space learned by AIRT AEs lacks structure, limiting their effectiveness in downstream defect characterization tasks. To address this limitation, this paper proposes a principal component analysis guided (PCA-guided) autoencoding framework for structured dimensionality reduction to capture intricate, non-linear features in thermographic signals while enforcing a structured latent space. A novel loss function, PCA distillation loss, is introduced to guide AIRT AEs to align the latent representation with structured PCA components while capturing the intricate, non-linear patterns in thermographic signals. To evaluate the utility of the learned, structured latent space, we propose a neural network-based evaluation metric that assesses its suitability for defect characterization. Experimental results show that the proposed PCA-guided AE outperforms state-of-the-art dimensionality reduction methods on PVC, CFRP, and PLA samples in terms of contrast, signal-to-noise ratio (SNR), and neural network-based metrics.

AI-driven pulse thermography (PT) has become a crucial tool in non-destructive testing (NDT), enabling automatic detection of hidden anomalies in various industrial components. Current state-of-the-art techniques feed segmentation and depth estimation networks compressed PT sequences using either Principal Component Analysis (PCA) or Thermographic Signal Reconstruction (TSR). However, treating these two modalities independently constrains the performance of PT inspection models as these representations possess complementary semantic features. To address this limitation, this work proposes PT-Fusion, a multi-modal attention-based fusion network that fuses both PCA and TSR modalities for defect segmentation and depth estimation of subsurface defects in PT setups. PT-Fusion introduces novel feature fusion modules, Encoder Attention Fusion Gate (EAFG) and Attention Enhanced Decoding Block (AEDB), to fuse PCA and TSR features for enhanced segmentation and depth estimation of subsurface defects. In addition, a novel data augmentation technique is proposed based on random data sampling from thermographic sequences to alleviate the scarcity of PT datasets. The proposed method is benchmarked against state-of-the-art PT inspection models, including U-Net, attention U-Net, and 3D-CNN on the Universit\'e Laval IRT-PVC dataset. The results demonstrate that PT-Fusion outperforms the aforementioned models in defect segmentation and depth estimation accuracies with a margin of 10%.

Background: In the absence of prospective data on diabetic foot ulcers (DFU), cross-sectional associations with causal risk factors (peripheral neuropathy, and peripheral arterial disease (PAD)) could be used to establish the validity of plantar thermography for DFU risk stratification. Methods: First, we investigated the associations between the intrinsic clusters of plantar thermographic images with several DFU risk factors using an unsupervised deep-learning framework. We then studied associations between obtained thermography clusters and DFU risk factors. Second, to identify those associations with predictive power, we used supervised learning to train Convolutional Neural Network (CNN) regression/classification models that predicted the risk factor based on the thermograph (and visual) input. Findings: Our dataset comprised 282 thermographs from type 2 diabetes mellitus patients (aged 56.31 +- 9.18 years, 51.42 % males). On clustering, we found two overlapping clusters (silhouette score = 0.10, indicating weak separation). There was strong evidence for associations between assigned clusters and several factors related to diabetic foot ulceration such as peripheral neuropathy, PAD, number of diabetes complications, and composite DFU risk prediction scores such as Martins-Mendes, PODUS-2020, and SIGN. However, models predicting said risk factors had poor performances. Interpretation: The strong associations between intrinsic thermography clusters and several DFU risk factors support the validity of using thermography for characterising DFU risk. However, obtained associations did not prove to be predictive, likely due to, spectrum bias, or because thermography and classical risk factors characterise incompletely overlapping portions of the DFU risk construct. Our findings highlight the challenges in standardising ground truths when defining novel digital biomarkers.

We present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. We demonstrated this principle by training the algorithm with data collected at different outdoors temperature, which lead to the detection of thermal bridges. The method can be implemented to assist human professionals during routine building inspections or combined with mobile platforms for automating examination of large areas.

Breast cancer is one of the most threatening diseases in women's life; thus, the early and accurate diagnosis plays a key role in reducing the risk of death in a patient's life. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural issues. Latest advances in computational tools, infrared cameras and devices for bio-impedance quantification, have given a chance to emerge other reference techniques like thermography, infrared thermography, electrical impedance tomography and biomarkers found in blood tests, therefore being faster, reliable and cheaper than other methods. In the last two decades, the techniques mentioned above have been considered as parallel and extended approaches for breast cancer diagnosis, as well many authors concluded that false positives and false negatives rates are significantly reduced. Moreover, when a screening method works together with a computational technique, it generate a "computer-aided diagnosis" system. The present work aims to review the last breakthroughs about the three techniques mentioned earlier, suggested machine learning techniques for breast cancer diagnosis, thus, describing the benefits of some methods in relation with other ones, such as, logistic regression, decision trees, random forest, deep and convolutional neural networks. With this, we studied several hyper-parameters optimization approaches with parzen tree optimizers to improve the performance of baseline models. An exploratory data analysis for each database and a benchmark of convolutional neural networks for the database of thermal images are presented.

This week's opinion piece is from technology influencer and futurist Dr Sarah-Jayne Gratton The latest statistics around breast cancer send a stark reminder of just how important early detection is in combating this brutal disease. With revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. One of the leading causes of death for cancer patients is a late diagnosis, too often brought about by inferior testing facilities, human factors, such as fatigue and loss of concentration, or by the patients themselves, who put off seeing a specialist due to the fear of what they might discover. But now, thanks to nothing short of revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. AI is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation.

Breast cancer awareness month is here and, with it, the latest statistics send a stark reminder of just how important early detection is in combating this brutal disease. With revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. One of the leading causes of death for cancer patients is a late diagnosis, too often brought about by inferior testing facilities, human factors, such as fatigue and loss of concentration, or by the patients themselves, who put off seeing a specialist due to the fear of what they might discover. But now, thanks to nothing short of revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. AI is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation.