4,420 research outputs found
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
Towards PACE-CAD Systems
Despite phenomenal advancements in the availability of medical image datasets and the development of modern classification algorithms, Computer-Aided Diagnosis (CAD) has had limited practical exposure in the real-world clinical workflow. This is primarily because of the inherently demanding and sensitive nature of medical diagnosis that can have far-reaching and serious repercussions in case of misdiagnosis. In this work, a paradigm called PACE (Pragmatic, Accurate, Confident, & Explainable) is presented as a set of some of must-have features for any CAD. Diagnosis of glaucoma using Retinal Fundus Images (RFIs) is taken as the primary use case for development of various methods that may enrich an ordinary CAD system with PACE. However, depending on specific requirements for different methods, other application areas in ophthalmology and dermatology have also been explored.
Pragmatic CAD systems refer to a solution that can perform reliably in day-to-day clinical setup. In this research two, of possibly many, aspects of a pragmatic CAD are addressed. Firstly, observing that the existing medical image datasets are small and not representative of images taken in the real-world, a large RFI dataset for glaucoma detection is curated and published. Secondly, realising that a salient attribute of a reliable and pragmatic CAD is its ability to perform in a range of clinically relevant scenarios, classification of 622 unique cutaneous diseases in one of the largest publicly available datasets of skin lesions is successfully performed.
Accuracy is one of the most essential metrics of any CAD system's performance. Domain knowledge relevant to three types of diseases, namely glaucoma, Diabetic Retinopathy (DR), and skin lesions, is industriously utilised in an attempt to improve the accuracy. For glaucoma, a two-stage framework for automatic Optic Disc (OD) localisation and glaucoma detection is developed, which marked new state-of-the-art for glaucoma detection and OD localisation. To identify DR, a model is proposed that combines coarse-grained classifiers with fine-grained classifiers and grades the disease in four stages with respect to severity. Lastly, different methods of modelling and incorporating metadata are also examined and their effect on a model's classification performance is studied.
Confidence in diagnosing a disease is equally important as the diagnosis itself. One of the biggest reasons hampering the successful deployment of CAD in the real-world is that medical diagnosis cannot be readily decided based on an algorithm's output. Therefore, a hybrid CNN architecture is proposed with the convolutional feature extractor trained using point estimates and a dense classifier trained using Bayesian estimates. Evaluation on 13 publicly available datasets shows the superiority of this method in terms of classification accuracy and also provides an estimate of uncertainty for every prediction.
Explainability of AI-driven algorithms has become a legal requirement after Europe’s General Data Protection Regulations came into effect. This research presents a framework for easy-to-understand textual explanations of skin lesion diagnosis. The framework is called ExAID (Explainable AI for Dermatology) and relies upon two fundamental modules. The first module uses any deep skin lesion classifier and performs detailed analysis on its latent space to map human-understandable disease-related concepts to the latent representation learnt by the deep model. The second module proposes Concept Localisation Maps, which extend Concept Activation Vectors by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier.
This thesis probes many viable solutions to equip a CAD system with PACE. However, it is noted that some of these methods require specific attributes in datasets and, therefore, not all methods may be applied on a single dataset. Regardless, this work anticipates that consolidating PACE into a CAD system can not only increase the confidence of medical practitioners in such tools but also serve as a stepping stone for the further development of AI-driven technologies in healthcare
Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
Skin lesions known as naevi exhibit diverse characteristics such as size,
shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into
play when monitoring for melanoma, referring to a lesion with distinctive
features that sets it apart from other lesions in the vicinity. As lesions
within the same individual typically share similarities and follow a
predictable pattern, an ugly duckling naevus stands out as unusual and may
indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD)
has become a significant player in the research and development field, as it
combines machine learning techniques with a variety of patient analysis
methods. Its aim is to increase accuracy and simplify decision-making, all
while responding to the shortage of specialized professionals. These automated
systems are especially important in skin cancer diagnosis where specialist
availability is limited. As a result, their use could lead to life-saving
benefits and cost reductions within healthcare. Given the drastic change in
survival when comparing early stage to late-stage melanoma, early detection is
vital for effective treatment and patient outcomes. Machine learning (ML) and
deep learning (DL) techniques have gained popularity in skin cancer
classification, effectively addressing challenges, and providing results
equivalent to that of specialists. This article extensively covers modern
Machine Learning and Deep Learning algorithms for detecting melanoma and
suspicious naevi. It begins with general information on skin cancer and
different types of naevi, then introduces AI, ML, DL, and CAD. The article then
discusses the successful applications of various ML techniques like
convolutional neural networks (CNN) for melanoma detection compared to
dermatologists' performance. Lastly, it examines ML methods for UD naevus
detection and identifying suspicious naevi
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Tissue-type plasminogen activator-primed human iPSC-derived neural progenitor cells promote motor recovery after severe spinal cord injury.
The goal of stem cell therapy for spinal cord injury (SCI) is to restore motor function without exacerbating pain. Induced pluripotent stem cells (iPSC) may be administered by autologous transplantation, avoiding immunologic challenges. Identifying strategies to optimize iPSC-derived neural progenitor cells (hiNPC) for cell transplantation is an important objective. Herein, we report a method that takes advantage of the growth factor-like and anti-inflammatory activities of the fibrinolysis protease, tissue plasminogen activator tPA, without effects on hemostasis. We demonstrate that conditioning hiNPC with enzymatically-inactive tissue-type plasminogen activator (EI-tPA), prior to grafting into a T3 lesion site in a clinically relevant severe SCI model, significantly improves motor outcomes. EI-tPA-primed hiNPC grafted into lesion sites survived, differentiated, acquired markers of motor neuron maturation, and extended βIII-tubulin-positive axons several spinal segments below the lesion. Importantly, only SCI rats that received EI-tPA primed hiNPC demonstrated significantly improved motor function, without exacerbating pain. When hiNPC were treated with EI-tPA in culture, NMDA-R-dependent cell signaling was initiated, expression of genes associated with stemness (Nestin, Sox2) was regulated, and thrombin-induced cell death was prevented. EI-tPA emerges as a novel agent capable of improving the efficacy of stem cell therapy in SCI
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
We propose a new method for breast cancer screening from DCE-MRI based on a
post-hoc approach that is trained using weakly annotated data (i.e., labels are
available only at the image level without any lesion delineation). Our proposed
post-hoc method automatically diagnosis the whole volume and, for positive
cases, it localizes the malignant lesions that led to such diagnosis.
Conversely, traditional approaches follow a pre-hoc approach that initially
localises suspicious areas that are subsequently classified to establish the
breast malignancy -- this approach is trained using strongly annotated data
(i.e., it needs a delineation and classification of all lesions in an image).
Another goal of this paper is to establish the advantages and disadvantages of
both approaches when applied to breast screening from DCE-MRI. Relying on
experiments on a breast DCE-MRI dataset that contains scans of 117 patients,
our results show that the post-hoc method is more accurate for diagnosing the
whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method
achieves an AUC of 0.81. However, the performance for localising the malignant
lesions remains challenging for the post-hoc method due to the weakly labelled
dataset employed during training.Comment: Submitted to Medical Image Analysi
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