Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band Imaging

To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep learning based methods for automatic polyp detection have been developed for conventional white-light imaging (WLI). Compared with WLI, narrow-band imaging (NBI) can improve polyp classification during colonoscopy but requires special equipment. We propose a CycleGAN-based framework to convert images captured with regular WLI to synthetic NBI (SNBI) as a pre-processing method for improving object detection on WLI when NBI is unavailable. This paper first shows that better results for polyp detection can be achieved on NBI compared to a relatively similar dataset of WLI. Secondly, experimental results demonstrate that our proposed modality translation can achieve improved polyp detection on SNBI images generated from WLI compared to the original WLI. This is because our WLI-to-SNBI translation model can enhance the observation of polyp surface patterns in the generated SNBI images

Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data

Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub

A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism

This work investigates the predictive potential of bipolar electroencephalogram (EEG) recordings towards efficient prediction of poor neurological outcomes. A retrospective design using a hybrid deep learning approach is utilized to optimize an objective function aiming for high specificity, i.e., true positive rate (TPR) with reduced false positives (< 0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly selected 5-minute segment in an hour is kept. In order to determine the outcome prediction, a combination of a feature encoder with 1-D convolutional layers, learnable position encoding, a context network with attention mechanisms, and finally, a regressor and classifier blocks are used. The feature encoder extricates local temporal and spatial features, while the following position encoding and attention mechanisms attempt to capture global temporal dependencies. Results: The proposed framework by our team, OUS IVS, when validated on the challenge hidden validation data, exhibited a score of 0.57.Comment: 4 pages, 3 figures, 50th Computing in Cardiology conference in Atlanta, Georgia, USA on 1st - 4th October 202

Kidney and tumor segmentation using combined Deep learning method

This paper presents our method for automatic segmentation for kidney and tumor as part of the Kidney Tumor Segmentation Challenge (KiTS19). The KiTS19 Challenge had released a dataset of 300 unique kidney cancer patients, with manual annotations done by Climb 4 Kidney Cancer (C4KC). Here we have proposed our new combined cascade deep learning (DL) approach for solving the tasks of the challenge. We used deep learning based detection for localising kidney with the tumor, followed by deep learning based segmentation to create the labels for kidney and tumor locally. Our approach resulted in high recall (96.13) and high Jacquard score (95.4) on the randomly selected 30 volumes that were picked as the validation set

Development of Image Processing Algorithms for the AutomaticScreening of Colon Cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers among both genders and its incidence rate is continuously increasing. CRC starts from small non-cancerous growths of tissue on the wall of the colon (large bowel) or rectum. Most polyps are harmless, but some can develop into CRC over time. Currently, colonoscopy is the golden standard method for the detection and removal of precancerous polyps. Colonoscopy, however, is an operator-dependent procedure and requires skilled endoscopists. Studies have shown that the polyp miss rate is around 25\% for certain cases. This miss rate has drawn the attention of engineers and computer scientists, including our group, for decades to develop a computer-aided polyp detection system that can help clinicians reduce this polyp miss rate during colonoscopy. This thesis has primarily contributed towards the investigation of the difficulties and challenges to develop an accurate automatic polyp detection and segmentation using deep learning approaches. Experimental results showed that deep learning is a promising approach to computerize colon polyp detection and segmentation, and it offers various approaches to improve the overall performance of the detection. In general, a massive amount of training data is the key to achieve desirable performance as there are already excellent CNN-based feature extractors. However, there is a lack of available training data, and manual polyp labeling of video frames is difficult and time-consuming. We showed that deep learning can be used to semi-automatically annotate video frames and produce 96\% of the Dice similarity score between the polyp masks provided by clinicians and the masks generated by our framework. We also showed that conditional GAN (CGAN) could be used to generate synthetic polyps to enlarge the training samples and improve the performance. The results demonstrated that deep learning-based models are vulnerable to small perturbations and noises. We found out that the bidirectional temporal information is essential to make CNN-based detection more reliable and less vulnerable

Eros y Tánatos, componentes esenciales en la cuentística de Germán Espinosa

Asistimos a un estudio sobre los elementos del Eros y del Tánatos como fenómenos fundamentales y dinamizadores de mundo en los personajes ficcionales de variados cuentos del escritor colombiano Germán Espinosa. Los protagonistas literarios y su devenir existencial en las diferentes situaciones de las narraciones abordadas, por estar sujetos a una serie de interdictos morales y sociales que una vez confrontados proyectan la fragilidad y el enigmatismo del hombre frente a la existencia misma y la confrontación con el otro, reflejan la condición del ser humano. Asimismo, se reconoce la palabra estética espinosiana como generadora de cuestionamientos frente a los recónditos misterios que rigen el comportamiento del hombre contemporáneo

Automatic colon polyp detection using region based deep CNN and post learning approaches

Automatic image detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this paper, we apply a recent region-based convolutional neural network (CNN) approach for the automatic detection of polyps in the images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods, such as automatic false positive learning and offline learning, both of which can be incorporated with the region-based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance than other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.publishedVersion(C) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information

Accurate Real-time Polyp Detection in Videos from Concatenation of Latent Features Extracted from Consecutive Frames

An efficient deep learning model that can be implemented in real-time for polyp detection is crucial to reducing polyp miss-rate during screening procedures. Convolutional neural networks (CNNs) are vulnerable to small changes in the input image. A CNN-based model may miss the same polyp appearing in a series of consecutive frames and produce unsubtle detection output due to changes in camera pose, lighting condition, light reflection, etc. In this study, we attempt to tackle this problem by integrating temporal information among neighboring frames. We propose an efficient feature concatenation method for a CNN-based encoder-decoder model without adding complexity to the model. The proposed method incorporates extracted feature maps of previous frames to detect polyps in the current frame. The experimental results demonstrate that the proposed method of feature concatenation improves the overall performance of automatic polyp detection in videos. The following results are obtained on a public video dataset: sensitivity 90.94%, precision 90.53%, and specificity 92.46%

Polyp detection and segmentation using Mask R-CNN: Does a deeper feature extractor CNN always perform better?

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extractor by adding extra polyp images to the training dataset to answer whether we need deeper and more complex CNNs, or better dataset for training in automatic polyp detection and segmentation. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance on the 2015 MICCAI polyp detection dataset. The best results achieved are 72.59% recall, 80% precision, 70.42% dice, and 61.24% jaccard. The model achieved state-of-the-art segmentation performance

Polyp detection and segmentation using Mask R-CNN: Does a deeper feature extractor CNN always perform better?

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape and texture. In this paper, we adapt Mask R-CNN and evaluate its performance with different modern convolutional neural networks (CNN) as its feature extractor for polyp detection and segmentation. We investigate the performance improvement of each feature extractor by adding extra polyp images to the training dataset to answer whether we need deeper and more complex CNNs, or better dataset for training in automatic polyp detection and segmentation. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance on the 2015 MICCAI polyp detection dataset. The best results achieved are 72.59% recall, 80% precision, 70.42% dice, and 61.24% jaccard. The model achieved state-of-the-art segmentation performance