This work introduces T-Loss, a novel and robust loss function for medical image segmentation. T-Loss is derived from the negative log-likelihood of the Student-t distribution and excels at handling noisy masks by dynamically controlling its sensitivity through a single parameter. This parameter is optimized during the backpropagation process, obviating the need for additional computations or prior knowledge about the extent and distribution of noisy labels. We provide in-depth analysis of this parameter behavior during training and revealing its adaptive nature and its role in preventing noisy memorization. Our extensive experiments demonstrate that T-Loss significantly outperforms traditional loss functions in terms of dice scores on two public medical datasets, specifically for skin lesion and lung segmentation. Moreover, T-Loss exhibits remarkable resilience to various types of simulated label noise, which mimics human annotation errors. Our results provide strong evidence that T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. The project website, including code and additional resources, can be found at: https://robust-tloss.github.io/.

Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance.In this paper, we revisit the task of data cleaning and formalize it as either a ranking problem, which significantly reduces human inspection effort, or a scoring problem, which allows for automated decisions based on score distributions.We find that a specific combination of context-aware self-supervised representation learning and distance-based indicators is effective in finding issues without annotation biases.This methodology, which we call SelfClean, surpasses state-of-the-art performance in detecting off-topic images, near duplicates, and label errors within widely-used image datasets, such as ImageNet-1k, Food-101N, and STL-10, both for synthetic issues and real contamination.We apply the detailed method to multiple image benchmarks, identify up to 16% of issues, and confirm an improvement in evaluation reliability upon cleaning.The official implementation can be found at: https://github.com/Digital-Dermatology/SelfClean.

Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier's accuracy is dependent on the quality of the OCT image. Therefore, image denoising plays an important role in having an accurate feedback system. A common OCT image denoising technique is the frame-averaging method. Inherent to this method is the need for multiple images, i.e., the more images used, the better the resulting image quality. However, this approach comes at the price of increased acquisition time and sensitivity to motion artifacts. To overcome these limitations, we applied a deep-learning denoising method capable of imitating the frame-averaging method. The resulting image had a similar image quality to the frame-averaging and was better than the classical digital filtering methods. We also evaluated if this method affects the tissue classifier model's accuracy that will provide feedback to the ablation laser. We found that image denoising significantly increased the accuracy of the tissue classifier. Furthermore, we observed that the classifier trained using the deep learning denoised images achieved similar accuracy to the classifier trained using frame-averaged images. The results suggest the possibility of using the deep learning method as a pre-processing step for real-time tissue classification in smart laser osteotomy.

Hand eczema is a frequent dermatosis with severe health and financial consequences to patients and society. It follows a chronic course and persists up to 15 years after onset. Early detection of an exacerbation followed by the application of specific drugs for a few days can considerably reduce disease activity and avoid temporary disability. However, dermatitis patients usually rely on their own perception in assessing their skin condition and therefore often miss the time point for effective treatment. In this paper we present a prototype-based feasibility study of automated detection and quantification of hand eczema using texton-based imaging and machine-learning techniques.