Saliency-driven system models for cell analysis with deep learning.

Background and objectivesSaliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert's visual attention, there is little evidence about how these models perform when used to predict the cytopathologist's eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions. This paper describes how our computational schemes retrieve regions of interest (ROI) of clinical relevance using visual attention models. We also compare the performance of different computed saliency models as part of cell screening tasks, aiming to design a computer-aided diagnosis systems that supports cytopathologists.MethodWe record eye fixation maps from cytopathologists at work, and compare with 13 different saliency prediction algorithms, including deep learning. We develop cell-specific convolutional neural networks (CNN) to investigate the impact of bottom-up and top-down factors on saliency prediction from real routine exams. By combining the eye tracking data from pathologists with computed saliency models, we assess algorithms reliability in identifying clinically relevant cells.ResultsThe proposed cell-specific CNN model outperforms all other saliency prediction methods, particularly regarding the number of false positives. Our algorithm also detects the most clinically relevant cells, which are among the three top salient regions, with accuracy above 98% for all diseases, except carcinoma (87%). Bottom-up methods performed satisfactorily, with saliency maps that enabled ROI detection above 75% for carcinoma and 86% for other pathologies.ConclusionsROIs extraction using our saliency prediction methods enabled ranking the most relevant clinical areas within the image, a viable data reduction strategy to guide automatic analyses of Pap smear slides. Top-down factors for saliency prediction on cell images increases the accuracy of the estimated maps while bottom-up algorithms proved to be useful for predicting the cytopathologist's eye fixations depending on parameters, such as the number of false positive and negative. Our contributions are: comparison among 13 state-of-the-art saliency models to cytopathologists' visual attention and deliver a method that the associate the most conspicuous regions to clinically relevant cells

Flipping the practice based pathology laboratory-can it support development of practitioner capability for trainee pathologists in gynaecological cytopathology?

This study investigated the role of 'flipping', the practice-based pathology laboratory and classroom to support the development of trainee pathologist practitioners' in the field of gynaecological cytopathology, addressing development of their knowledge and practical application in the clinical setting. Content-rich courses traditionally involve lecture led delivery which restricts tutors from adopting approaches that support greater student engagement in the topic area and application of knowledge to practice. We investigated the role of 'flipping', the practice-based pathology laboratory and classroom where 'virtual lectures' were accessed outside of 'class time' allowing more time for students to engage in active learning under the supervision of a consultant histopathologist. 'Flipping' was used to support two gynaecological cytopathology training courses with cohorts of eight trainee pathologists on the first course and six on the second. Lectures were made available to the trainees to watch before attending the workshops. The workshops consisted of group activities and individual practical exercises allowing trainees to review and report on patient practice cases with the support of their peers and tutors. Focus group sessions were held after each course, allowing trainee pathologists to reflect on their experiences. Discussions were transcribed and thematic analysis was used to capture key themes discussed by the trainees. Trainees' identified that 'flipping' provided them with more time during face-to-face sessions, enabling a greater depth of questioning and engagement with the consultant histopathologists. Having already watched the lectures, trainees were able to attend the sessions having identified areas in which they needed additional support and development. Trainee pathologists reported they had more time to concentrate on developing their skills and practise under the guidance of the consultant histopathologists so developing their capability in gynaecological cytopathology. The role of alternative methods of delivery such as 'flipping' is suggested for short courses designed to support practitioner capability and continued professional development

Custom Deep Learning Model for the Diagnosis of Cervical Carcinoma

Cancer is the second most common cause of death in the majority of the world due to late diagnosis. Most cancer cases are typically discovered at an advanced stage, which lowers the likelihood of recovery because proper therapy cannot be given at that time. In particular, for incurable cancers, which may result in a reduced life expectancy due to the rapid progression of the disease, the sooner cancer is identified, the more effective the therapy may be. Early detection also lessens the financial effects of cancer because treatment in the early stages is much cheaper than treatment in later stages.The method suggested is an end-to-end deep learning method in which the input photos are sent directly to the deep model, which makes the decision. The proposed Ensemble of deep learning modelIV3-DCNN to detect cancer in pap-test images. The model's precision, FScore, Specificity, Sensitivity, and accuracy of 99.4%, 99.23, 95.48, 97.9, and 99.2%. Last but not least, the suggested strategy would be very beneficial and successful, especially in low-income nations where referral mechanisms for patients with suspected cancer are frequently lacking, resulting in delayed and fragmented care

Towards Interpretable Machine Learning in Medical Image Analysis

Over the past few years, ML has demonstrated human expert level performance in many medical image analysis tasks. However, due to the black-box nature of classic deep ML models, translating these models from the bench to the bedside to support the corresponding stakeholders in the desired tasks brings substantial challenges. One solution is interpretable ML, which attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, interpretability is not a property of the ML model but an affordance, i.e., a relationship between algorithm and user. Thus, prototyping and user evaluations are critical to attaining solutions that afford interpretability. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and end users. To overcome the predicament, we first define 4 levels of clinical evidence that can be used to justify the interpretability to design ML models. We state that designing ML models with 2 levels of clinical evidence: 1) commonly used clinical evidence, such as clinical guidelines, and 2) iteratively developed clinical evidence with end users are more likely to design models that are indeed interpretable to end users. In this dissertation, we first address how to design interpretable ML in medical image analysis that affords interpretability with these two different levels of clinical evidence. We further highly recommend formative user research as the first step of the interpretable model design to understand user needs and domain requirements. We also indicate the importance of empirical user evaluation to support transparent ML design choices to facilitate the adoption of human-centered design principles. All these aspects in this dissertation increase the likelihood that the algorithms afford interpretability and enable stakeholders to capitalize on the benefits of interpretable ML. In detail, we first propose neural symbolic reasoning to implement public clinical evidence into the designed models for various routinely performed clinical tasks. We utilize the routinely applied clinical taxonomy for abnormality classification in chest x-rays. We also establish a spleen injury grading system by strictly following the clinical guidelines for symbolic reasoning with the detected and segmented salient clinical features. Then, we propose the entire interpretable pipeline for UM prognostication with cytopathology images. We first perform formative user research and found that pathologists believe cell composition is informative for UM prognostication. Thus, we build a model to analyze cell composition directly. Finally, we conduct a comprehensive user study to assess the human factors of human-machine teaming with the designed model, e.g., whether the proposed model indeed affords interpretability to pathologists. The human-centered design process is proven to be truly interpretable to pathologists for UM prognostication. All in all, this dissertation introduces a comprehensive human-centered design for interpretable ML solutions in medical image analysis that affords interpretability to end users

CyTest – An Innovative Open-source Platform for Training and Testing in Cythopathology

Abstract This paper describes an e-learning platform developed in the context of the European Project CyTest (2014-1-IT01-KA202-002607), dedicated to Cytological Training at European Standard through Telepathology. The main, and novel, feature of our system is the deep integration between virtual microscopy and the training system: images are not simply there to be seen but they are active parts of testing, supporting quantitative measurement of image comprehension, for instance by evaluating the identification of relevant cellular structures by the position of markers put by the student on the image. The solution we developed offers a complete tool for easy creation and interactive access to questions related to images and fully integrates the components of virtual microscopy and teaching, based on state-of-the-art instruments for digital pathology images management, as OMERO, and for training course distribution, as Moodle. The system can be easily extended to support histopathological diagnosis. The software is distributed as Open Source and available on GitHub