Line Probe Assay (LPA) is a widely used method for diagnosing drug-resistant tuberculosis (DRTB), but it is a time-consuming and labor-intensive process that requires expert interpretation. DRTB is a significant threat to global TB control efforts and its prompt diagnosis is critical for initiating appropriate treatment. In this paper, we present an automated LPA test interpretation solution that uses computer vision techniques to extract and analyze strips from LPA sheets and uses machine learning algorithms to produce drug sensitivity and resistivity outcomes with extremely high precision and recall. We also develop OCR models to eliminate manual data entry to further reduce the overall time. Our solution comprises a rejection module that flags ambiguous and novel samples that are then referred to experienced lab technicians. This results in increased trust in the solution. To evaluate our solution, we curate an extensive and diverse dataset of LPA strips annotated by multiple microbiologists across India. Our solution achieves more than 95% accuracy for all drugs on this dataset. The proposed solution has the potential to increase the efficiency, standardization of LPA test interpretation, and fast-tracking the dissemination of results to end-users via a designated Management Information System (MIS).

The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of the vector. Motivated by recent advances in compressive sensing with generative models, where a generative modeling assumption replaces the usual sparsity assumption, we study the problem of 1-bit compressive sensing with generative models. We first consider noiseless 1-bit measurements, and provide sample complexity bounds for approximate recovery under i.i.d. Gaussian measurements and a Lipschitz continuous generative prior, as well as a near-matching algorithm-independent lower bound. Moreover, we demonstrate that the Binary $\epsilon$-Stable Embedding property, which characterizes the robustness of the reconstruction to measurement errors and noise, also holds for 1-bit compressive sensing with Lipschitz continuous generative models with sufficiently many Gaussian measurements. In addition, we apply our results to neural network generative models, and provide a proof-of-concept numerical experiment demonstrating significant improvements over sparsity-based approaches.