PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2019, Hyderabad, Indi

Comparison of two high dose rate brachytherapy regimes

Background: Two HDR brachytherapy regimes were compared, 9.5 Gray per fraction for two fractions and 7.5 Gray per fraction for three fractions. Materials and Methods: A total of 80 patients with histologically evident squamous cell carcinoma cervix were taken in the current research after randomization. Radiotherapy dose delivered was 50 Gray/25#/5 weeks with concomitant chemotherapy with weekly cisplatin 35 mg/m2. Following external chemoradiation, patients were randomized into two arms. In Arm A, 40 patients were given high dose rate (HDR) brachytherapy of weekly 7.5 Gray in three fractions over 3 weeks. In Arm B, 40 patients were given high dose rate (HDR) brachytherapy of weekly 9.5 Gray in two fractions over 2 weeks. BED and LQED had been calculated, and the evaluation of response and consequences was examined. Results: In Arm A, BED to point A was 99.38 ± 0.00 and EQD2 to point A was 82.81 ± 0.00. In Arm B, BED to point A was 97.05 ± 0.00 and EQD2 to point A was 80.89 ± 0.00. With respect to rectum in Arm A, BED rectum was 108.66 ± 11.43 and EQD2 rectum was 65.07 ± 6.84. In Arm B, BED rectum was 107 ± 10.83 and EQD2 rectum was 64.21 ± 6.49. Similarly in Arm A, BED bladder was 107.86 ± 10.23 and EQD2 bladder was 64.59 ± 6.13. In arm B, BED bladder was 104.14 ± 10.79 and EQD2 bladder was 62.36 ± 6.46. Conclusion: In a follow-up of 6 months, no statistical significance in terms of local control as well as complications rates in both the arms. Our research demonstrates that two fractions of HDR are comparable with three fractions of HDR