Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning

Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.

Exploiting Deep Learning Techniques for Colon Polyp Segmentation

As colon cancer is among the top causes of death, there is a growinginterest in developing improved techniques for the early detection of colonpolyps. Given the close relation between colon polyps and colon cancer,their detection helps avoid cancer cases. The increment in the availability ofcolorectal screening tests and the number of colonoscopies have increasedthe burden on the medical personnel. In this article, the application of deeplearning techniques for the detection and segmentation of colon polyps incolonoscopies is presented. Four techniques were implemented and evaluated:Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC).These were trained and tested using CVC-Colon database, ETIS-LARIBPolyp, and a proprietary dataset. Three experiments were conducted to assessthe techniques performance: (1) Training and testing using each databaseindependently, (2) Mergingd the databases and testing on each database independently using a merged test set, and (3) Training on each dataset and testingon the merged test set. In our experiments, PANet architecture has the bestperformance in Polyp detection, and HTC was the most accurate to segmentthem. This approach allows us to employ Deep Learning techniques to assisthealthcare professionals in the medical diagnosis for colon cancer. It is anticipated that this approach can be part of a framework for a semi-automatedpolyp detection in colonoscopies.

A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer

A new framework for early diagnosis of prostate cancer using Diffusion-Weighted Imaging (DWI) is proposed. The proposed diagnostic approach consists of the following four steps to detect locations that are suspicious for prostate cancer: 1) In the first step, we isolate the prostate from the surrounding anatomical structures based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures);2) In order to take into account any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to local motion, a non-rigid registration algorithm is employed;3) A KNN-based classifier is used to classify the prostate into benign or malignant based on three appearance features extracted from registered images;and 4) The tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Preliminary experiments on 28 patients (17 malignant and 11 benign) resulted in 100% correct classification, showing that the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of prostate cancer.