Anatomical landmark identification is crucial in the registration of medical images in a wide range of clinical applications. Various machine and deep learning (DL) techniques have been proposed to annotate anatomical landmarks automatically. However, very few have taken advantage of the more recent Transformer models that do not suffer from inductive bias of convolutional neural networks as well as incorporating uncertainty assessments. This paper proposes a novel technique based on the Swin Transformer V2 (Swin-V2) with an uncertainty quantification module for robust anatomical landmark detection in paraspinal muscle MRIs to facilitate muscle morphometric analysis for low back pain (LBP). Specifically, we employ statistical measurements from stochastic sampling using Monte Carlo (MC) dropouts as uncertainty metrics for automatic landmark detection. The proposed method was trained and validated using 350 axial MRI slices of paraspinal muscles at the mid-disc spinal levels commonly associated with LBP. We demonstrate that the selected uncertainty metrics are correlated with automatic landmark detection errors and, in addition, can be used as inputs to grade the quality of the identified landmarks with a lightweight random forest algorithm for a more straightforward interpretation.

Intracranial hemorrhage (ICH) is a life-threatening medical emergency caused by various factors. Timely and precise diagnosis of ICH is crucial for administering effective treatment and improving patient survival rates. While deep learning techniques have emerged as the leading approach for medical image analysis and processing, the most commonly employed supervised learning often requires large, high-quality annotated datasets that can be costly to obtain, particularly for pixel/voxel-wise image segmentation. To address this challenge and facilitate ICH treatment decisions, we proposed a novel weakly supervised ICH segmentation method that leverages a hierarchical combination of head-wise gradient-infused self-attention maps obtained from a Swin transformer. The transformer is trained using an ICH classification task with categorical labels. To build and validate the proposed technique, we used two publicly available clinical CT datasets, namely RSNA 2019 Brain CT hemorrhage and PhysioNet. Additionally, we conducted an exploratory study comparing two learning strategies -binary classification and full ICH subtyping -to assess their impact on self-attention and our weakly supervised ICH segmentation framework. The proposed algorithm was compared against the popular U-Net with full supervision, as well as a similar weakly supervised approach using Grad-CAM for ICH segmentation. With a mean Dice score of 0.47, our technique achieved similar ICH segmentation performance as the U-Net and outperformed the Grad-CAM based approach, demonstrating the excellent potential of the proposed framework in challenging medical image segmentation tasks.