Medical time-series data play a vital role in disease diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead to overfitting. To address these challenges, we propose DAAC (Discrepancy-Aware Adaptive Contrastive learning), a learnable multi-view contrastive framework that integrates external normal samples and enhances feature learning through adaptive contrastive strategies. DAAC consists of two key modules: (1) a Discrepancy Estimator, built upon a GAN-enhanced encoder-decoder architecture, captures the distribution of normal data and computes reconstruction errors as indicators of abnormality. These discrepancy features augment the target dataset to mitigate overfitting. (2) an Adaptive Contrastive Learner uses multi-head attention to extract discriminative representations by contrasting embeddings across multiple views and data granularities (subject, trial, epoch, and temporal levels), eliminating the need for handcrafted positive-negative sample pairs. Extensive experiments on three clinical datasets—covering Alzheimer’s disease, Parkinson’s disease, and myocardial infarction—demonstrate that DAAC significantly outperforms existing methods, even when only 10\% of labeled data is available, showing strong generalization and diagnostic performance. Our code is available at https://github.com/CUHKSZ-MED-BioE/DAAC.

Machine learning (ML) techniques are popular in many parameter estimation tasks; however, they face challenges in the real-world deployment due to the lack of robustness to errors. ML estimators are not able to ascertain performance in the presence of noise, variations in the data distribution, and anomalies in the test samples. This work proposes a novel framework, surrogate-based physical error correction (SPEC), which addresses the unmet need for measurement reliability estimation and self-correction under process data uncertainty, by bringing together physics- and network-based optimization. The workings of SPEC are demonstrated using the paradigm of gas parameter estimation in the laser absorption spectroscopy (LAS). It operates in two modes, estimation and correction. During estimation, SPEC provides an initial state estimate, with estimation reliability being assessed by the physics-driven anomaly detection (PAD) module, which uses a hybrid error, combining a nondifferentiable reconstruction error, calculated through an ensemble network, and a differentiable feasibility error. When an estimate is flagged as unreliable, the correction mode is enabled. This network-based optimization algorithm delivers efficient and robust state correction by using a greedy ensemble search. SPEC's performance is evaluated in a variety of experiments including outside-of-distribution and noisy data. Moreover, it offers reconfigurability through PAD configuration modification, eliminating the need for ML estimator retraining.

Electrical Impedance Tomography based sensors have emerged as a promising approach in tactile sensing, offering notable advantages such as affordability, portability, and low power consumption. However, the inherently ill-posed nature of the inverse problem often results in reconstruction errors, impacting on the accuracy of tactile information retrieval. In this work, an effective deep learning approach for tactile sensing is proposed, leveraging the concept of learnable masks, incorporated within a Convolutional Neural Network. The learnable masks support the selection of the most informative feature subsets from the associated voltage inputs, enabling the network to reconstruct conductivity distributions precisely. The proposed approach exhibited outstanding performance in image reconstruction, achieving a mean square error of 0.000041, a structural similarity index of 98.28, and a peak signal-to-noise ratio of 42.35 dB.

When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design. This work focuses on detecting and correcting failed state estimations before adopting them in SAE inverse problems, by utilizing simulations and performance metrics guided by physical laws. We suggest to flag a machine learning estimation when its physical model error exceeds a feasible threshold, and propose a novel approach, GEESE, to correct it through optimization, aiming at delivering both low error and high efficiency. The key designs of GEESE include (1) a hybrid surrogate error model to provide fast error estimations to reduce simulation cost and to enable gradient based backpropagation of error feedback, and (2) two generative models to approximate the probability distributions of the candidate states for simulating the exploitation and exploration behaviours. All three models are constructed as neural networks. GEESE is tested on three real-world SAE inverse problems and compared to a number of state-of-the-art optimization/search approaches. Results show that it fails the least number of times in terms of finding a feasible state correction, and requires physical evaluations less frequently in general.