Multimodal Deep Learning for Cervical Dysplasia Diagnosis

A survey on deep learning in medical image analysis

“…Several researchers have shown that designing architectures incorporating unique task-specific properties can obtain better results than straightforward CNNs. Two examples which we encountered several times are multi-view Gao et al (2016d) Frame labeling US CNN 4 class frame classification using transfer learning with pre-trained networks Kumar et al (2016) Frame labeling US CNN 12 standard anatomical planes, CNN extracts features for support vector machine Rajchl et al (2016b) Segmentation with non expert labels MRI CNN Crowd-sourcing annotation efforts to segment brain structures Rajchl et al (2016a) Segmentation given bounding box MRI CNN CNN and CRF for segmentation of structures Ravishankar et al (2016a) Quantification US CNN Hybrid system using CNN and texture features to find abdominal circumference Yu et al (2016b) Left ventricle segmentation US CNN Frame-by-frame segmentation by dynamically fine-tuning CNN to the latest frame Wound segmentation photographs CNN Additional detection of infection risk and healing progress Ypsilantis et al (2015) Chemotherapy response prediction PET CNN CNN outperforms classical radiomics features in patients with esophageal cancer Zheng et al (2015) Carotid artery bifurcation detection CT CNN Two stage detection process, CNNs combined with Haar features Alansary et al (2016) Placenta segmentation MRI CNN 3D multi-stream CNN with extension for motion correction Fritscher et al (2016) Head&Neck tumor segmentation CT CNN 3 orthogonal patches in 2D CNNs, combined with other features Jaumard- Hakoun et al (2016) Tongue contour extraction US RBM Analysis of tongue motion during speech, combines auto-encoders with RBMs Payer et al (2016) Hand landmark detection X-ray CNN Various architectures are compared Quinn et al (2016) Disease detection microscopy CNN Smartphone mounted on microscope detects malaria, tuberculosis & parasite eggs Smistad and Løvstakken (2016) Vessel detection and segmentation US CNN Femoral and carotid vessels analyzed with standard fCNN Twinanda et al (2017) Task recognition in laparoscopy Videos CNN Fine-tuned AlexNet applied to video frames Xu et al (2016c) Cervical dysplasia cervigrams CNN Fine-tuned pre-trained network with added non-imaging features Xue et al (2016) Esophageal microvessel classification Microscopy CNN Simple CNN used for feature extraction Zhang et al (2016a) Image reconstruction CT CNN Reconstructing from limited angle measurements, reducing reconstruction artefacts Lekadir et al (2017) Carotid plaque classification US CNN Simple CNN for characterization of carotid plaque composition in ultrasound …”

Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

“…A recent trend in this field is deep learning (LeCun, Bengio & Hinton, 2015), which involves large neural network architectures with successive applications of such functions. Deep learning, in fact, has been able to provide accurate predictions of patient diagnosis in multiple medical domains (Xu et al, 2016;Chicco, Sadowski & Baldi, 2014;Fernandes, Cardoso & Astrup, 2017a;Cangelosi et al, 2016;Alipanahi et al, 2015). We applied our learning scheme to deep variational autoencoders and feed-forward neural networks.…”

“…However, cervical cancer can be prevented by means of the human papillomavirus infection (HPV) vaccine, and regular low-cost screening programs (Centers for Disease Control and Prevention (CDC), 2013). The two most widespread techniques in screening programs are conventional or liquid cytology and colposcopy (Fernandes, Cardoso & Fernandes, 2015;Plissiti & Nikou, 2013;Fernandes, Cardoso & Fernandes, 2017b;Xu et al, 2016). Furthermore, this cancer can be cured by removing the affected tissues when identified in early stages (Fernandes, Cardoso & Fernandes, 2015; Centers for Disease Control and Prevention (CDC), 2013), in most cases.…”

Deep Learning for Cancer Screening in Medical Imaging

“…In 2016, several conference papers published improved results on cervical dysplasia diagnosis and cervical cancer classification [13]. Because cervical cancer ranks as the second most common type of cancer in women aged 15 to 44 years worldwide, these results have very important implications for women's health.…”