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"cancer detection": models, code, and papers

Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging

May 14, 2022
David Ahmedt-Aristizabal, Chuong Nguyen, Lachlan Tychsen-Smith, Ashley Stacey, Shenghong Li, Joseph Pathikulangara, Lars Petersson, Dadong Wang

Modern data-driven machine learning research that enables revolutionary advances in image analysis has now become a critical tool to redefine how skin lesions are documented, mapped, and tracked. We propose a 3D whole body imaging prototype to enable rapid evaluation and mapping of skin lesions. A modular camera rig arranged in a cylindrical configuration is designed to automatically capture synchronised images from multiple angles for entire body scanning. We develop algorithms for 3D body image reconstruction, data processing and skin lesion detection based on deep convolutional neural networks. We also propose a customised, intuitive and flexible interface that allows the user to interact and collaborate with the machine to understand the data. The hybrid of the human and computer is represented by the analysis of 2D lesion detection, 3D mapping and data management. The experimental results using synthetic and real images demonstrate the effectiveness of the proposed solution by providing multiple views of the target skin lesion, enabling further 3D geometry analysis. Skin lesions are identified as outliers which deserve more attention from a skin cancer physician. Our detector identifies lesions at a comparable performance level as a physician. The proposed 3D whole body imaging system can be used by dermatological clinics, allowing for fast documentation of lesions, quick and accurate analysis of the entire body to detect suspicious lesions. Because of its fast examination, the method might be used for screening or epidemiological investigations. 3D data analysis has the potential to change the paradigm of total-body photography with many applications in skin diseases, including inflammatory and pigmentary disorders.


Automatic Polyp Segmentation via Multi-scale Subtraction Network

Aug 11, 2021
Xiaoqi Zhao, Lihe Zhang, Huchuan Lu

More than 90\% of colorectal cancer is gradually transformed from colorectal polyps. In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Therefore, automatic polyp segmentation techniques are of great importance for both patients and doctors. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate localization and blurred edges of polyps. To address this challenge, we propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image. Specifically, we first design a subtraction unit (SU) to produce the difference features between adjacent levels in encoder. Then, we pyramidally equip the SUs at different levels with varying receptive fields, thereby obtaining rich multi-scale difference information. In addition, we build a training-free network "LossNet" to comprehensively supervise the polyp-aware features from bottom layer to top layer, which drives the MSNet to capture the detailed and structural cues simultaneously. Extensive experiments on five benchmark datasets demonstrate that our MSNet performs favorably against most state-of-the-art methods under different evaluation metrics. Furthermore, MSNet runs at a real-time speed of $\sim$70fps when processing a $352 \times 352$ image. The source code will be publicly available at \url{}. \keywords{Colorectal Cancer \and Automatic Polyp Segmentation \and Subtraction \and LossNet.}

* This work was accepted by MICCAI 2021 

Beyond Social Media Analytics: Understanding Human Behaviour and Deep Emotion using Self Structuring Incremental Machine Learning

Sep 05, 2020
Tharindu Bandaragoda

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.


Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images

Dec 07, 2021
André Pedersen, Erik Smistad, Tor V. Rise, Vibeke G. Dale, Henrik S. Pettersen, Tor-Arne S. Nordmo, David Bouget, Ingerid Reinertsen, Marit Valla

Histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for semantic segmentation of gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumour segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for postprocessing the generated tumour segmentation heatmaps. The overall best design achieved a Dice score of 0.933 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872) and a low-resolution U-Net (0.874). In addition, segmentation on a representative x400 WSI took ~58 seconds, using only the CPU. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.

* 12 pages, 3 figures 

Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images

Mar 12, 2022
Frederic Madesta, Thilo Sentker, Tobias Gauer, Rene Werner

4D CT imaging is an essential component of radiotherapy of thoracic/abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality. In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas. The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. Evaluation is based on 65 in-house 4D CT data sets of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and the publicly available DIRLab 4D CT data (independent external test set). Automated artifact detection revealed a ROC-AUC of 0.99 for INT and 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 60% (DS) and 42% (INT) for the in-house evaluation data (simulated artifacts for the slight artifact data; original data were considered as ground truth for RMSE computation). For the external DIR-Lab data, the RMSE decreased by 65% and 36%, respectively. Applied to the pronounced artifact data group, on average 68% of the detectable artifacts were removed. The results highlight the potential of DL-based inpainting for the restoration of artifact-affected 4D CT data. Improved performance of conditional inpainting (compared to standard inpainting) illustrates the benefits of exploiting patient-specific prior knowledge.

* 10 pages, 8 figures 

More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination

Jan 07, 2021
Jian Dai, Shuge Lei, Licong Dong, Xiaona Lin, Huabin Zhang, Desheng Sun, Kehong Yuan

Objective: Breast cancer screening is of great significance in contemporary women's health prevention. The existing machines embedded in the AI system do not reach the accuracy that clinicians hope. How to make intelligent systems more reliable is a common problem. Methods: 1) Ultrasound image super-resolution: the SRGAN super-resolution network reduces the unclearness of ultrasound images caused by the device itself and improves the accuracy and generalization of the detection model. 2) In response to the needs of medical images, we have improved the YOLOv4 and the CenterNet models. 3) Multi-AI model: based on the respective advantages of different AI models, we employ two AI models to determine clinical resuls cross validation. And we accept the same results and refuses others. Results: 1) With the help of the super-resolution model, the YOLOv4 model and the CenterNet model both increased the mAP score by 9.6% and 13.8%. 2) Two methods for transforming the target model into a classification model are proposed. And the unified output is in a specified format to facilitate the call of the molti-AI model. 3) In the classification evaluation experiment, concatenated by the YOLOv4 model (sensitivity 57.73%, specificity 90.08%) and the CenterNet model (sensitivity 62.64%, specificity 92.54%), the multi-AI model will refuse to make judgments on 23.55% of the input data. Correspondingly, the performance has been greatly improved to 95.91% for the sensitivity and 96.02% for the specificity. Conclusion: Our work makes the AI model more reliable in medical image diagnosis. Significance: 1) The proposed method makes the target detection model more suitable for diagnosing breast ultrasound images. 2) It provides a new idea for artificial intelligence in medical diagnosis, which can more conveniently introduce target detection models from other fields to serve medical lesion screening.

* 12 pages, 6 figures, 6 tables 

OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting

Aug 17, 2021
Michael Fitzke, Derick Whitley, Wilson Yau, Fernando Rodrigues Jr, Vladimir Fadeev, Cindy Bacmeister, Chris Carter, Jeffrey Edwards, Matthew P. Lungren, Mark Parkinson

Background: Histopathology is an important modality for the diagnosis and management of many diseases in modern healthcare, and plays a critical role in cancer care. Pathology samples can be large and require multi-site sampling, leading to upwards of 20 slides for a single tumor, and the human-expert tasks of site selection and and quantitative assessment of mitotic figures are time consuming and subjective. Automating these tasks in the setting of a digital pathology service presents significant opportunities to improve workflow efficiency and augment human experts in practice. Approach: Multiple state-of-the-art deep learning techniques for histopathology image classification and mitotic figure detection were used in the development of OncoPetNet. Additionally, model-free approaches were used to increase speed and accuracy. The robust and scalable inference engine leverages Pytorch's performance optimizations as well as specifically developed speed up techniques in inference. Results: The proposed system, demonstrated significantly improved mitotic counting performance for 41 cancer cases across 14 cancer types compared to human expert baselines. In 21.9% of cases use of OncoPetNet led to change in tumor grading compared to human expert evaluation. In deployment, an effective 0.27 min/slide inference was achieved in a high throughput veterinary diagnostic pathology service across 2 centers processing 3,323 digital whole slide images daily. Conclusion: This work represents the first successful automated deployment of deep learning systems for real-time expert-level performance on important histopathology tasks at scale in a high volume clinical practice. The resulting impact outlines important considerations for model development, deployment, clinical decision making, and informs best practices for implementation of deep learning systems in digital histopathology practices.


2.75D Convolutional Neural Network for Pulmonary Nodule Classification in Chest CT

Feb 11, 2020
Ruisheng Su, Weiyi Xie, Tao Tan

Early detection and classification of pulmonary nodules in Chest Computed tomography (CT) images is an essential step for effective treatment of lung cancer. However, due to the large volume of CT data, finding nodules in chest CT is a time consuming thus error prone task for radiologists. Benefited from the recent advances in Convolutional Neural Networks (ConvNets), many algorithms based on ConvNets for automatic nodule detection have been proposed. According to the data representation in their input, these algorithms can be further categorized into: 2D, 3D and 2.5D which uses a combination of 2D images to approximate 3D information. Leveraging 3D spatial and contextual information, the method using 3D input generally outperform that based on 2D or 2.5D input, whereas its large memory footprints becomes the bottleneck for many applications. In this paper, we propose a novel 2D data representation of a 3D CT volume, which is constructed by spiral scanning a set of radials originated from the 3D volume center, referred to as the 2.75D. Comparing to the 2.5D, the 2.75D representation captures omni-directional spatial information of a 3D volume. Based on 2.75D representation of 3D nodule candidates in Chest CT, we train a convolutional neural network to perform the false positive reduction in the nodule detection pipeline. We evaluate the nodule false positive reduction system on the LUNA16 data set which contains 1186 nodules out of 551,065 candidates. By comparing 2.75D with 2D, 2.5D and 3D, we show that our system using 2.75D input outperforms 2D and 2.5D, yet slightly inferior to the systems using 3D input. The proposed strategy dramatically reduces the memory consumption thus allow fast inference and training by enabling larger number of batches comparing to the methods using 3D input.


Confidence Aware Neural Networks for Skin Cancer Detection

Jul 24, 2021
Donya Khaledyan, AmirReza Tajally, Ali Sarkhosh, Afshar Shamsi, Hamzeh Asgharnezhad, Abbas Khosravi, Saeid Nahavandi

Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities. However, Deep Neural Networks (DNNs) require a huge amount of data, and because of the lack of sufficient data in this field, transfer learning can be a great solution. DNNs used for disease diagnosis meticulously concentrate on improving the accuracy of predictions without providing a figure about their confidence of predictions. Knowing how much a DNN model is confident in a computer-aided diagnosis model is necessary for gaining clinicians' confidence and trust in DL-based solutions. To address this issue, this work presents three different methods for quantifying uncertainties for skin cancer detection from images. It also comprehensively evaluates and compares performance of these DNNs using novel uncertainty-related metrics. The obtained results reveal that the predictive uncertainty estimation methods are capable of flagging risky and erroneous predictions with a high uncertainty estimate. We also demonstrate that ensemble approaches are more reliable in capturing uncertainties through inference.

* 21 Pages, 7 Figures, 2 Tables