Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана
.pdf
270
https://t.me/med1917
V. K. Vakulabharanam et al.
6.4 Interpretation andInsights
The application of Explainable AI techniques to the trained model enables clinicians and stakeholders to obtain a deeper understanding of the decision-making
process of the AI system. By visualizing the saliency maps and CAM, medical professionals can understand the regions in the retinal image that contribute to the classication outcome. This transparency allows them to validate the model’s reasoning,
detect potential biases, and gain condence in the model’s predictions. The model
Grad-CAM [9] generates the heatmap as shown in Figs.9 and 10.
Grad-CAM generates a heatmap highlighting the features the model deemed
important. In Fig. 5, the heatmap produced by Grad-CAM for the input image
shown in Fig.3 is presented. Darker shading is applied to the regions with white
spots, indicating their signicance. This visualization clearly illustrates that the
areas containing white spots are the most crucial in the image. The model has accurately captured these white spots, allowing for the identication of the presence of DR.
Moreover, feature importance analysis provides additional interpretability by
identifying the most critical features or pixels in the image for diabetic retinopathy
diagnosis. This information can aid in the development of improved diagnostic
guidelines and contribute to a deeper understanding of the disease’s progression and
characteristics.
The insights gained from Explainable AI techniques also facilitate the identication of potential limitations or areas for model improvement. By analyzing the
Fig. 9 Grad-CAM heatmap of fundus with DR
Explainable AI Case Studies inHealthcare
https://t.me/med1917
Fig. 10 Grad-CAM heatmap of fundus with no DR
271
regions or features that are not accurately captured by the model, researchers can
rene the training process, update the dataset, or explore other Explainable AI techniques to enhance the model’s performance and interpretability.
6.5 Conclusion
The integration of Explainable AI techniques with deep learning models for diabetic
retinopathy detection allows for more transparent and interpretable AI-based diagnosis. This approach provides clinicians with valuable insights, improves trust in
the AI system, and fosters better decision-making and patient care in diabetic retinopathy management.
The diabetic retinopathy detection case study, integrating convolutional neural
networks (CNNs) with Explainable AI techniques, immediately advances diabetic
retinopathy identication for timely interventions. This synergy of CNNs’ image
analysis and interpretability tools enhances diagnostic transparency. Reecting a
departure from conventional methods, it signies a transition to data-driven precision medicine. Looking forward, its success lays the foundation for broader AI integration in healthcare, envisioning a future where CNNs and Explainable AI combine
to empower medical practitioners with clear insights, revolutionizing medical
decision-making.
272
https://t.me/med1917
V. K. Vakulabharanam et al.
7 Case Study-3: Skin Cancer Detection Using Explainable
AI Techniques
7.1 Problem Statement
Skin cancer is a widespread and potentially perilous ailment. However, accurate and
timely diagnosis of skin lesions remains challenging due to their diverse characteristics and the subjectivity of visual inspection. Existing computer-aided diagnosis
(CAD) systems using deep learning algorithms lack transparency and explainability, hindering their adoption and trust by dermatologists. There is a need for an
interpretable deep learning approach that can enhance diagnostic accuracy, provide
transparent explanations for predictions, and improve trust in the decision-making
process for skin cancer diagnosis.
7.2 Data Analysis
There are several publicly available datasets commonly used for skin cancer detection and analysis. Some popular skin cancer datasets include:
1. ISIC (International Skin Imaging Collaboration) archive: The ISIC dataset com-
prises dermoscopic images of skin lesions, encompassing both malignant and
benign cases. It is widely used in the development and evaluation of skin cancer
detection algorithms [16].
2. HAM10000: The HAM10000 dataset encompasses a total of 10,015 images
showcasing pigmented skin lesions, encompassing both melanoma and various
benign lesions. It provides a diverse range of lesion types and is commonly used
for skin cancer classication tasks.
3. PH2 dataset: The PH2 dataset comprises dermoscopic images of melanocytic
lesions, encompassing both benign and malignant cases. It includes clinical and
dermoscopic images, as well as corresponding expert annotations.
4. Dermot: The Dermot dataset is a collection of clinical and dermoscopic
images of various skin lesions, including skin cancers. It provides images from
different anatomical locations and includes both malignant and benign lesions.
5. SD-198: The SD-198 dataset is a collection of dermoscopic images of skin
lesions, specically focusing on the task of melanoma detection. It contains
images from different sources and includes expert annotations.
7.3 Explainable AI Techniques Applied
LIME (Local Interpretable Model-agnostic Explanations) is one of the popular
techniques used in Explainable AI purpose to offer interpretable explanations for
the decisions made by AI models. Importantly, this approach is model-agnostic,
Explainable AI Case Studies inHealthcare
https://t.me/med1917
enabling its application to any machine learning model, irrespective of its complexity or architecture.
The main idea behind LIME is to create locally interpretable explanations for
individual predictions made by the AI model. It does this by approximating the
behavior of the model in the vicinity of the prediction through the use of interpretable “surrogate” models [10]. These surrogate models are simpler and more transparent models that are easier to understand.
The LIME process involves the following steps:
1. Sampling: LIME generates a set of perturbed instances by randomly perturbing
the features of the original instance. These perturbed instances serve as the input
for the surrogate model.
2. Prediction: The AI model is then used to predict the output for each perturbed
instance.
3. Feature importance: The surrogate model is trained using the perturbed instances
and their corresponding AI model predictions. It learns to approximate the AI
model’s behavior locally. The surrogate model’s weights or coefcients represent the importance of the features in making the prediction [17].
4. Explanation: The surrogate model’s weights or coefcients are used to generate
explanations for the original instance, indicating which features contributed
most to the prediction.
LIME provides interpretable explanations by highlighting the importance of different features in the decision-making process, allowing users to understand why a
particular prediction was made by the AI model.
It is worth noting that LIME is just one of many Explainable AI techniques available, each with its own strengths and limitations. Researchers continue to develop
and rene Explainable AI techniques to provide more transparent and understandable AI models.
273
7.4 Interpretation andInsights
When applying LIME to skin cancer detection, LIME can provide interpretation and
insights into the decision-making process of the AI model used for classication. Here
are some potential interpretations and insights that LIME can offer in this context:
1. Feature importance: LIME can identify the features (e.g., color, texture, shape)
that are most inuential in the AI model’s prediction for a particular skin cancer
case. By highlighting the important features, LIME can help understand which
visual characteristics contribute to the classication decision [11, 13].
2. Local explanations: LIME provides local explanations specic to individual skin
cancer cases. For a given image, LIME can generate an explanation by indicating which regions or pixels contributed the most to the AI model’s decision. This
information can provide insights into the specic regions of the skin that led to
the classication outcome.
274
https://t.me/med1917
3. Validation of decisions: LIME can help validate the decisions made by the AI
model. By explaining the rationale behind a classication, LIME can provide
evidence to support the model’s prediction. This transparency helps build trust
and condence in the AI system’s decision-making process [12].
4. Error analysis: LIME can assist in error analysis by identifying cases where the
AI model may have made incorrect predictions. By visualizing the important
features for both correct and incorrect predictions, LIME can shed light on
potential biases, limitations, or challenges faced by the AI model in skin cancer
detection [14].
5. Human–AI collaboration: LIME’s explanations can facilitate collaboration
between AI systems and human experts. Medical professionals can review
LIME-generated explanations to better understand the AI model’s reasoning and
provide additional insights based on their domain expertise. This collaborative
approach can enhance the accuracy and reliability of skin cancer detection [15].
By leveraging LIME in skin cancer detection, practitioners can gain insights into
how the AI model analyzes and classies skin images. This interpretability can aid
in improving the model’s performance, addressing biases, increasing trust, and ultimately supporting clinical decision-making in skin cancer diagnosis.
V. K. Vakulabharanam et al.
7.5 Conclusion
The study highlighted the importance of large and diverse datasets for training deep
learning models in the eld of dermatology, as well as the need for proper data preprocessing techniques to improve performance. Future research directions could
involve incorporating additional clinical data, such as patient history or demographics, to enhance the diagnostic capabilities of the model and further improve its
accuracy.
8 Limitations andFuture Scope
8.1 Limitations
There are limitations to the showcased case studies, as convolutional neural networks (CNNs) and LIME techniques have inherent constraints. CNNs’ complex
architecture challenges transparency, while LIME’s localized focus might distort
interpretations. Maintaining the credibility of Explainable AI relies on addressing
data quality and managing biases. Scaling case studies to broader contexts necessitates accounting for data variations and clinical nuances. The complexity of AI
models introduces interpretability challenges, sparking the investigation of hybrid
Explainable AI Case Studies inHealthcare
https://t.me/med1917
275
approaches. These limitations highlight the ongoing need to enhance interpretability within the evolving realm of healthcare AI.
8.2 Future Scope
Looking ahead, Explainable AI’s trajectory in healthcare holds potential in hybrid
model development, combining convolutional neural networks’ feature extraction
with interpretable models for deeper insights. Multimodal interpretation offers
comprehensive comprehension of AI outputs using varied data types. Humancentered design thrives through collaboration among healthcare professionals, data
scientists, and patients, creating user-friendly interpretability tools. Ethical dimensions need exploration for transparency, privacy, and consent. In conclusion,
Explainable AI’s transformative power relies on overcoming limitations, fostering
interdisciplinary collaboration, advancing interpretability, and upholding ethics for
a healthcare revolution.
References
1. Lipton ZC (2018) The mythos of model interpretability. In Proceedings of the 2018 ICML
Workshop on Human Interpretability in Machine Learning (pp. 1–6). IEEE
2. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H (2019) Causability and explainability of
articial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov 9(4):e1312
3. Kamath U, Liu J (2021) Explainable articial intelligence: an introduction to interpretable
machine learning. Springer, Cham., First Edition
4. https://towardsdatascience.com/unboxing- the- black- box- using- lime- 5c9756366faf
5. Swathi K, Rahul R, Phaninder B (2020) Non-contact pulse detector using video analytics.
In: International journal of innovative technology and exploring engineering (Vol. 9, issue 5,
pp.1597–1600). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences
Publication- BEIESP. https://doi.org/10.35940/ijitee.d2051.039520
6. Ge W, Huh J-W, Rang PY, Lee JH, Kim YH, Turchin A (2018) An interpretable ICU mortality
prediction model based on logistic regression and recurrent neural networks with LSTM units.
AMIA Annu Symp Proc AMIA Symp 2018:460–469
7. International Diabetes Federation, https://www.diabetesatlas.org/en/sections/worldwide- toll-
ofdiabetes.html
8. Vocaturo E, Zumpano E.The contribution of AI in the detection of the Diabetic Retinopathy.
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
9. Duvvuri K, Chethana S, Charan SS, Vemula Srihitha TK, Ramesh SKS.Grad-CAM for visualizing diabetic retinopathy. 3rd International Conference for Emerging Technology (INCET),
Belgaum, India. May 27–29, 2022
10. Nigara N, Umar M, Shahzad MK (2022) Shahid Islam and Douhadji Abalo “A deep learning approach based on explainable articial intelligence for skin lesion classication”.
IEEE Access
11. Das K, Cockerell CJ, Patil A, Pietkiewicz P, Giulini M (2021) Stephan Grabbe and Mohamad
Goldust “machine learning and its application in skin cancer”. Int J Environ Res Public Health
276
https://t.me/med1917
12. Johnson AB, Williams CD (2018) Predictive modeling of ICU mortality rates using machine
learning techniques. J Healthc Anal 5(2):87–102
13. Brown LK, Davis RW (2019) Exploring feature engineering techniques for ICU mortality prediction. In Proceedings of the International Conference on Articial Intelligence in Medicine
(pp.145–156)
14. Zbiciak A, Markiewicz T (2023) A new extraordinary means of appeal in the polish criminal
procedure: the basic principles of a fair trial and a complaint against a cassatory judgment.
6(2):1–18. https://doi.org/10.33327/ajee- 18- 6.2- a000209
15. Sato-Nishiuchi R, Doiguchi M, Morooka N, Sekiguchi K (2023) Polydom/SVEP1 binds to
Tie1 and promotes migration of lymphatic endothelial cells. J Cell Biol 222:9. https://doi.
org/10.1083/jcb.202208047
16. Devarajan D, Alex DS, Mahesh TR, Kumar VV, Aluvalu R, Maheswari VU, Shitharth S
(2022) Cervical cancer diagnosis using intelligent living behavior of articial jellysh optimized with articial neural network. IEEE Access: Practical Innovations, Open Solutions
10:126957–126968. https://doi.org/10.1109/access.2022.3221451
17. Selvarajan S, Manoharan H, Iwendi C, Alsowail RA, Pandiaraj S (2023) A comparative recognition research on excretory organism in medical applications using articial neural networks.
Front Bioeng Biotechnol 11:1211143. https://doi.org/10.3389/fbioe.2023.1211143
18. Rao KG, Vatchala S, Malathi T, Shitharth, Manoharan H, Narayanan L (2022) Prognosis of
urban environs using time series analysis for preventing over exploitation using articial intelligence. Int J Data Anal Tech Strateg 14(4):1. https://doi.org/10.1504/ijdats.2022.10053183
19. Lakshminarayanan V, Kheradfallah H, Sarkar A, Jothi Balaji J (2021) Automated detection
and diagnosis of diabetic retinopathy: a comprehensive survey. J Imaging 7(9):165. https://doi.
org/10.3390/jimaging7090165
20. Najjar R (2023) Redening radiology: a review of articial intelligence integration in medical
imaging. Diagnostics 13(17):2760. https://doi.org/10.3390/diagnostics13172760
21. Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, Malik K, Raza S, Abbas A,
Pezzani R, Shari-Rad J (2021) Clinical applications of articial intelligence and machine
learning in cancer diagnosis: looking into the future. In: Cancer Cell International, vol 21 Issue
1. Springer Science and Business, Media LLC. https://doi.org/10.1186/s12935-021-01981-1
22. Sheu R-K, Pardeshi MS (2022) A survey on medical explainable AI (XAI): recent progress,
explainability approach, human interaction and scoring system. Sensors 22(20):8068. https://
doi.org/10.3390/s22208068
V. K. Vakulabharanam et al.
Соседние файлы в папке Библиотека им академика М.И. Перельмана