Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays

The two-stage detection-after-segmentation model improves the accuracy of identifying subdiaphragmatic lesions

Chest X-rays (CXRs) are primarily used to detect lung lesions. While the abdominal portion of CXRs can sometimes reveal critical conditions, research in this area is limited. To address this, we introduce a two-stage architecture that separates the abdominal region from the CXR and detects abdominal lesions using a specialized dataset. We compared the performance of our method on whole CXRs versus isolated abdominal regions. First, we created masks for the right upper quadrant (RUQ), left upper quadrant (LUQ), and upper abdomen (ABD) regions and trained corresponding segmentation models for each area. For detecting abdominal lesions, we curated a dataset of 5,996 images, categorized into 19 classes based on anatomical locations, air patterns, and levels of stomach or bowel dilation. The detection process was initially conducted on the original images, followed by the three regional areas, RUQ, LUQ, and ABD, extracted by the segmentation models. The results showed that the detection model trained on the entire ABD region achieved the highest accuracy, followed closely by the RUQ and LUQ models. In contrast, the CXR model had the lowest accuracy. This study highlights that the two-stage architecture effectively manages distinct segmentation and detection tasks in CXRs, offering a promising avenue for more accurate diagnoses. It also suggests that an optimal ratio between the sizes of the target lesions and the input images may exist.

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.

Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential solution, to reduce the black-box effect of DL models and increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated with DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their variable quality, as well as constraints associated with real-world clinical routine. Moreover, we discuss the concept of structural uncertainty, a corpus of methods to facilitate the alignment of segmentation uncertainty estimates with clinical attention. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges for uncertainty quantification in the medical field.