Abstract
3D models are crucial in predicting aesthetic outcomes in breast reconstruction, supporting personalized surgical planning, and improving patient communication. In response to this necessity, this is the first application of Radiance Fields to 3D breast reconstruction. Building on this, the work compares six SoTA 3D reconstruction models. It introduces a novel variant tailored to medical contexts: Depth-Splatfacto, designed to improve denoising and geometric consistency through pseudo-depth supervision. Additionally, we extended model training to grayscale, which enhances robustness under grayscale-only input constraints. Experiments on a breast cancer patient dataset demonstrate that Splatfacto consistently outperforms others, delivering the highest reconstruction quality (PSNR 27.11, SSIM 0.942) and the fastest training times (×1.3 faster at 200k iterations). At the same time, the depth-enhanced variant offers an efficient and stable alternative with minimal fidelity loss. The grayscale train improves speed by ×1.6 with a PSNR drop of 0.70. Depth-Splatfacto further improves robustness, reducing PSNR variance by 10% and making images less blurry across test cases. These results establish a foundation for future clinical applications, supporting personalized surgical planning and improved patient-doctor communication. © 2025 Elsevier B.V., All rights reserved.

Abstract
Reject option is a technique used to improve classifier’s reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been concerned with the development of reject option mechanisms to be used by supervised learning architectures (e.g., MLP, LVQ or SVM). In this paper, however, we aim at proposing alternatives to this view, which are based on the self-organizing map (SOM), originally an unsupervised learning scheme, but that has also been successfully used in the design of prototype-based classifiers. The basic hypothesis we defend is that it is possible to design SOM-based classifiers endowed with reject option mechanisms whose performances are comparable to or better than those achieved by standard supervised classifiers. For this purpose, we carried out a comprehensively evaluation of the proposed SOM-based classifiers on two synthetic and three real-world datasets. The obtained results suggest that the proposed SOM-based classifiers consistently outperform standard supervised classifiers. © 2015 The Natural Computing Applications Forum

Abstract