Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs)
– one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.
Automated human authentication using features extracted from palmprint images has been studied extensively in the literature. Primary focus of the studies thus far has been the improvement of matching performance. As more biometric systems get deployed for wide range of applications, the threat of impostor attacks on these systems is on the rise. The most common among various types of attacks is the sensor level spoof attack using fake hands created using different materials. This paper investigates an approach for securing palmprint based biometric systems against spoof attacks that use photographs of the human hand for circumventing the system. The approach is based on the analysis of local texture patterns of acquired palmprint images for extracting discriminatory features. A trained binary classifier utilizes the discriminating information to determine if the input image is of real hand or a fake one. Experimental results, using 611 palmprint images corresponding to 100 subjects in the publicly available IITD palmprint image database, show that 1) palmprint authentication systems are highly vulnerable to spoof attacks and 2) the proposed spoof detection approach is effective for discriminating between real and fake image samples. In particular, the proposed approach achieves the best classification accuracy of 97.35%.
Using semiclassical time dependent perturbation treatment, the coherence radiation-semiconductor interaction under ultrashort pulsed near band-gap resonant excitation regime has been analytically investigated in a narrow direct-gap semiconductor waveguide structure. The role of excitonic effect is incorporated to study transient pulse propagation effects in GAs/AlGaAs waveguide duly irradiated by a 100 fs Ti:Sapphire laser. Nonlinear Schroedinger equation is employed to examine the role of group velocity dispersion and nonlinear optical effect on the transmission characteristics of the pulse at various excitation intensities and waveguide lengths. The results suggest the occurrence of pulse break-up and pulse narrowing during propagation of the pulse through the GaAs/AlGaAs waveguide.
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