Region proposals for saliency map refinement for weakly-supervised disease localisation and classification

First Online: 29 September 2020The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global annotations, making the localisation of diseases a weakly supervised approach. The main methods designed for weakly supervised disease classification and localisation rely on saliency or attention maps that are not specifically trained for localisation, or on region proposals that can not be refined to produce accurate detections. In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation. Using the ChestX-ray14 data set, we show that our proposed model establishes the new state-of-the-art for weakly-supervised disease diagnosis and localisation. We make our code available at https://github.com/renato145/RpSalWeaklyDet.Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneir

Post-hoc overall survival time prediction from brain MRI

Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to contain such precise segmentation, those SOTA methods have limited applicability. In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training. Our model uses medical image and patient demographics (represented by age) as inputs to estimate the OS time and to estimate a saliency map that localizes the tumor as a way to explain the OS time prediction in a post-hoc manner. It is worth emphasizing that although our model can localize tumors, it uses only the ground truth OS time as training signal, i.e., no segmentation labels are needed. We evaluate our post-hoc method on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 data set and show that it achieves competitive results compared to pre-hoc methods with the advantage of not requiring segmentation labels for training. We make our code available at https://github.com/renato145/posthocOS.Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneir

Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI

There is a heated debate on how to interpret the decisions provided by deep learning models (DLM), where the main approaches rely on the visualization of salient regions to interpret the DLM classification process. However, these approaches generally fail to satisfy three conditions for the problem of lesion detection from medical images: 1) for images with lesions, all salient regions should represent lesions, 2) for images containing no lesions, no salient region should be produced, and 3) lesions are generally small with relatively smooth borders. We propose a new model-agnostic paradigm to interpret DLM classification decisions supported by a novel definition of saliency that incorporates the conditions above. Our model-agnostic 1-class saliency detector (MASD) is tested on weakly supervised breast lesion detection from DCE-MRI, achieving state-of-the-art detection accuracy when compared to current visualization methods.Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley, Ian Reid, Gustavo Carneir

Training medical image analysis systems like radiologists

The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training set, and test it on a hold-out test set. This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning. In this paper, we propose a novel training approach inspired by how radiologists are trained. In particular, we explore the use of meta-training that models a classifier based on a series of tasks. Tasks are selected using teacher-student curriculum learning, where each task consists of simple classification problems containing small training sets. We hypothesize that our proposed meta-training approach can be used to pre-train medical image analysis models. This hypothesis is tested on the automatic breast screening classification from DCE-MRI trained with weakly labeled datasets. The classification performance achieved by our approach is shown to be the best in the field for that application, compared to state of art baseline approaches: DenseNet, multiple instance learning and multi-task learning.Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, and Gustavo Carneir

Semi-supervised multi-domain multi-task training for metastatic colon lymph node diagnosis from abdominal CT

The diagnosis of the presence of metastatic lymph nodes from abdominal computed tomography (CT) scans is an essential task performed by radiologists to guide radiation and chemotherapy treatment. State-of-the-art deep learning classifiers trained for this task usually rely on a training set containing CT volumes and their respective image-level (i.e., global) annotation. However, the lack of annotations for the localisation of the regions of interest (ROIs) containing lymph nodes can limit classification accuracy due to the small size of the relevant ROIs in this problem. The use of lymph node ROIs together with global annotations in a multi-task training process has the potential to improve classification accuracy, but the high cost involved in obtaining the ROI annotation for the same samples that have global annotations is a roadblock for this alternative. We address this limitation by introducing a new training strategy from two data sets: one containing the global annotations, and another (publicly available) containing only the lymph node ROI localisation. We term our new strategy semi-supervised multi-domain multi-task training, where the goal is to improve the diagnosis accuracy on the globally annotated data set by incorporating the ROI annotations from a different domain. Using a private data set containing global annotations and a public data set containing lymph node ROI localisation, we show that our proposed training mechanism improves the area under the ROC curve for the classification task compared to several training method baselines.Saskia Glaser, Gabriel Maicas, Sergei Bedrikovetski, Tarik Sammour, Gustavo Carneir

Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign

Objectives: Pouch of Douglas (POD) obliteration is a severe consequence of inflammation in the pelvis, often seen in patients with endometriosis. The sliding sign is a dynamic transvaginal ultrasound (TVS) test that can diagnose POD obliteration. We aimed to develop a deep learning (DL) model to automatically classify the state of the POD using recorded videos depicting the sliding sign test. Methods: Two expert sonologists performed, interpreted, and recorded videos of consecutive patients from September 2018 to April 2020. The sliding sign was classified as positive (i.e. normal) or negative (i.e. abnormal; POD obliteration). A DL model based on a temporal residual network was prospectively trained with a dataset of TVS videos. The model was tested on an independent test set and its diagnostic accuracy including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value (PPV/NPV) was compared to the reference standard sonologist classification (positive or negative sliding sign). Results: In a dataset consisting of 749 videos, a positive sliding sign was depicted in 646 (86.2%) videos, whereas 103 (13.8%) videos depicted a negative sliding sign. The dataset was split into training (414 videos), validation (139), and testing (196) maintaining similar positive/negative proportions. When applied to the test dataset using a threshold of 0.9, the model achieved: AUC 96.5% (95% CI: 90.8-100.0%), an accuracy of 88.8% (95% CI: 83.5-92.8%), sensitivity of 88.6% (95% CI: 83.0-92.9%), specificity of 90.0% (95% CI: 68.3-98.8%), a PPV of 98.7% (95% CI: 95.4-99.7%), and an NPV of 47.7% (95% CI: 36.8-58.2%). Conclusions: We have developed an accurate DL model for the prediction of the TVS-based sliding sign classification. Lay summary: Endometriosis is a disease that affects females. It can cause very severe scarring inside the body, especially in the pelvis - called the pouch of Douglas (POD). An ultrasound test called the 'sliding sign' can diagnose POD scarring. In our study, we provided input to a computer on how to interpret the sliding sign and determine whether there was POD scarring or not. This is a type of artificial intelligence called deep learning (DL). For this purpose, two expert ultrasound specialists recorded 749 videos of the sliding sign. Most of them (646) were normal and 103 showed POD scarring. In order for the computer to interpret, both normal and abnormal videos were required. After providing the necessary inputs to the computer, the DL model was very accurate (almost nine out of every ten videos was correctly determined by the DL model). In conclusion, we have developed an artificial intelligence that can interpret ultrasound videos of the sliding sign that show POD scarring that is almost as accurate as the ultrasound specialists. We believe this could help increase the knowledge on POD scarring in people with endometriosis.Gabriel Maicas, Mathew Leonardi, Jodie Avery, Catrina Panuccio, Gustavo Carneiro, M Louise Hull, and George Condou

Photoshopping colonoscopy video frames

The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps – such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame – the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. To test our framework, we use a new colonoscopy data set with 14317 images, split as a training set with 13350 images without polyps, and a testing set with 290 abnormal images containing polyps and 677 normal images without polyps. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.Yuyuan Liu, Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W. Verjans, Gustavo Carneir

Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: a systematic review and meta-analysis

PURPOSE: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies. METHODOLOGY: Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool. RESULTS: In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist's interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810-0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744-0.852) for colorectal malignancy. CONCLUSION: Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist's assessment. Deep learning models may further improve on this, but data remain limited.Sergei Bedrikovetski, Nagendra N. Dudi-Venkata, Gabriel Maicas, Hidde M. Kroon, Warren Seow, Gustavo Carneiro ... et al