We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.

Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literature.

Objectives: We aimed to develop and evaluate a novel computer-aided detection (CADe) approach for identifying small metastatic biochemically recurrent (BCR) prostate cancer (PCa) lesions on PSMA-PET images, utilizing multi-angle Maximum Intensity Projections (MA-MIPs) and state-of-the-art (SOTA) object detection algorithms. Methods: We fine-tuned and evaluated 16 SOTA object detection algorithms (selected across four main categories of model types) applied to MA-MIPs as extracted from rotated 3D PSMA-PET volumes. Predicted 2D bounding boxes were back-projected to the original 3D space using the Ordered Subset Expectation Maximization (OSEM) algorithm. A fine-tuned Medical Segment-Anything Model (MedSAM) was then also used to segment the identified lesions within the bounding boxes. Results: The proposed method achieved a high detection performance for this difficult task, with the FreeAnchor model reaching an F1-score of 0.69 and a recall of 0.74. It outperformed several 3D methods in efficiency while maintaining comparable accuracy. Strong recall rates were observed for clinically relevant areas, such as local relapses (0.82) and bone metastases (0.80). Conclusion: Our fully automated CADe tool shows promise in assisting physicians as a “second reader” for detecting small metastatic BCR PCa lesions on PSMA-PET images. By leveraging the strength and computational efficiency of 2D models while preserving 3D spatial information of the PSMA-PET volume, the proposed approach has the potential to improve detectability and reduce workload in cancer diagnosis and management.