Medical imaging based on radioactive tracers exposes the patient to radiation. For this reason, a phantom is preferably used for non-clinical studies such as routine quality assurance and research. The aim of this project was to design, build and test a multi-compartment phantom to be used in dynamic SPECT/CT, PET/CT and PET/MRI studies. By treating each compartment as a biological system and plotting activity distribution, desired characteristics of the phantom can be obtained. A software program was created to simulate compartment activity distribution for different input parameters. Such parameters include number of compartments, administered activity, flow rates between compartments and compartment volume. Based on the simulation, the phantom was designed to meet the desired characteristics. Due to the outbreak of the SARS-CoV-2 virus, no phantom could be built nor tested. Consequently, leading the project to create a foundation that facilitates future building of the phantom. / Medicinsk avbildning med radioaktiva spårämnen utsätter patienter för en stråldos. Av detta skäl används företrädesvis en fantom för icke-kliniska studier såsom rutinmässig kvalitetssäkring och forskning. Syftet med detta projekt var att designa, bygga och testa ett flerkammarfantom som ska användas i dynamiska SPECT/CT, PET/CT och PET/MRI studier. Genom att behandla varje kammare som ett biologiskt system och plotta aktivitetsfördelning kan önskade egenskaper hos fantomen erhållas. Ett program skapades för att simulera aktivitetsdistributionen i flerkammarfantomer för olika in parametrar så som antal kammare, administrerad aktivitet, flöden mellan kammare och kammarvolym. Baserat på simuleringen utformades fantomen för att uppfylla de önskade egenskaperna. På grund av utbrottet av SARS-CoV-2 viruset kunde ingen fantom byggas eller testas. Följaktligen leddes projektet till att skapa en grund som underlättar framtida byggande av fantomen.

Phase-contrast imaging (PCI) is a modality of medical x-ray imaging that can solve one of the main limitations with conventional attenuation-based imaging: the imaging of materials with low attenuation coefficients, such as soft tissues. A modality of PCI, Propagation-based phase-contrast imaging (PBI), was used in this project. This method does not require any optical elements than those used in the conventional imaging; it does, however, require more processing compared to other kinds of PCI. In addition to the reduced image quality, the required image reconstruction process, with PCI, also requires several manual adjustments, which in turn results in a lot of time consuming. In order to achieve that, a simple iterative image reconstruction method that combines Simultaneous Iterative Reconstruction Technique (SIRT) and propagation-based phase-contrast imaging was developed. The proposed method was compared with another commonly used phase-retrieval method, Paganin's algorithm. The obtained results showed higher resolution and reduced blur artefacts compared with Paganin's method. The developed method also appeared to be less sensitive to error in the input parameters, such as the attenuation coefficient, but also more time-consumption than the non-iterative Paganin's method, due to the higher data processing. / Faskontrastavbildning är en ny medicinsk röntgenavbildningsteknik, som har utvecklats för att ge bättre kontrast än konventionell röntgenavbildning, särskilt för objekt med låg attenuationskoefficient, såsom mjuk vävnad. I detta projekt användes s.k. propagationsbaserad faskonstrantavbildning, som är en av de enkla metoder som möjliggör faskontrastavbildningen, utan extra optiska element än det som ingår i en konventionell avbildning. Metoden kräver dock mer avancerad bildbehandling. Två av de huvudsakliga problemen som oftast uppstår vid faskontrastavbildning är minskad bildkvalité efter den väsentliga bildrekonstruktionen, samt att den är tidskrävande p.g.a. manuella justeringar som måste göras. I det här projektet implementerades en enkel metod baserad på en kombination av den iterativa algoritmen för bildrekonstruktion, Simultaneous Iterative Reconstruction Technique (SIRT), med propagationsbaserad faskonstrantavbildning. Resultaten jämfördes med en annan fasåterhämtningsmetod, som är välkänd och ofta används inom detta område, Paganinsmetod. Efter jämförelsen konstaterades att upplösningen blev högre och artefakter som suddighet reducerades. Det noterades också att den utvecklade metoden var mindre känslig för manuell inmatning av parametern för attenuationskoefficient. Metoden visade sig dock vara mer tidskrävande än Paganin-metoden.

Radiation and medical drugs are used to fight head and neck cancer, but unfortunately in some cases these treatments cause development of other diseases and injuries. Osteoradionecrosis (ORN) and medical-related osteonecrosis of the jaw (MRONJ) are dreaded late complications in jaws from radiation therapy and medical drugs and cause great suffering to those affected. The full extent of ORN and MRONJ may be difficult to diagnose due to visualizing problems in quantifying boundaries of osteonecrosis and healthy tissues. Maxillofacial surgeons now use radiology and clinical appearance to differ affected bone, which may result in unprecise estimation of the area that is affected. As a possible adjuvant diagnostic procedure, visualizing osteonecrosis by examining neutrophil elastase (NE) activity in jaws was tested in patients. A newly developed positron emission tomography (PET) tracer specific for NE was used for observation and measurement in PET/CT images. An image processing software was used for visualization, segmentation, and analysis. Areas with osteonecrosis were identified in the ORN patients, but not in their entirety and all activity could not be equated with osteonecrosis as undiagnosed areas as well absorbed the tracer. Visualization of MRONJ displayed unexpectedly low activity in the diagnosed area.    The conclusion drawn from the results and the analysis is that NE activity can be found in osteonecrosis patients, but the activity itself does not provide complete information to visualize and quantify the diseased area and it cannot be equated with osteonecrosis. To verify NE activity as osteonecrosis, tissue samples from the affected area need to be collected for histological examination

Radiotherapy is a common and useful method for treating prostate cancer, often using gold fiducial markers in the prostate as guidance. However, when there is a high risk of lymph node involvement, the independent motion of volumes causes complications in patient positioning since there is a choice between position against the gold fiducial markers or the bone anatomy. This leads to expansion of margins for either the prostate or the pelvic lymph nodes. In this thesis two different treatment strategies were performed and compared against given treatment plans. The purpose was to evaluate the standard treatment and to be able to recommend a new clinical approach for treatment of high-risk prostate cancer. Nine high-risk prostate cancer patients with their given treatment plans were used as a baseline. The patients underwent a planning CT and five CBCTs during the treatment. Two new treatment plan setups were done, a robust treatment and a sequential treatment with three and nine different plans respectively. The baseline and the robust treatment used gold fiducial markers as reference, with a prescribed dose of 2.20 Gy over 35 fractions with a VMAT. The sequential treatment used both gold fiducial markers and bone anatomy as reference, done by 35 fractions with a prescribed dose of 0.6 Gy with a single arc and 1.6 Gy with a dual arc respectively. A total of thirteen different treatment plan setups for each patient were simulated 100 times each, resulting in 11700 simulated treatments in total. The resulting simulated treatments were evaluated by the percentage passing nine different clinical goals, as well as dose and percentage volume averages for these goals. The results from the simulated robust treatments showed a decrease in percentage passing and D98 for the prostate and an increase in percentage passing and D98 for the lymph nodes and vesicles compared to the baseline. An increase in percentage passing and D98 was seen in the sequential treatment strategy for both targets compared to the baseline. The rectum had a larger percentage passing the clinical goals and a lower V69, V74 and V59 for both the robust and sequential treatment strategies. The D2 for the external were lower in the robust treatment strategy but higher in the sequential treatment strategy, while the D2 to the femoral heads were lower for both compared to the baseline treatment strategy. In conclusion, an improved dose coverage was seen in the sequential strategy with good sparing of risk organs. The robust treatment strategy showed promising results for sparing risk organs, but with a less robust dose coverage of the prostate.

Vitiligo is a skin disease where the pigment cells that produce melanin die or stop functioning, which causes white patches to appear on the body. Although vitiligo is not considered a serious disease, there is a risk that something is wrong with a person's immune system. In recent years, the use of medical image processing techniques has grown, and research continues to develop new techniques for analysing and processing medical images. In many medical image classification tasks, deep convolutional neural network technology has proven its effectiveness, which means that it may also perform well in vitiligo classification. Our study uses four deep convolutional neural networks in order to classify images of vitiligo and normal skin. The architectures selected are VGG-19, ResNeXt101, InceptionResNetV2 and Inception V3. ROC and AUC metrics are used to assess each model's performance. In addition, the authors investigate the economic benefits that this technology may provide to the healthcare system and patients. To train and evaluate the CNN models, the authors used a dataset that contains 1341 images in total. Because the dataset is limited, 5-fold cross validation is also employed to improve the model's prediction. The results demonstrate that InceptionV3 achieves the best performance in the classification of vitiligo, with an AUC value of 0.9111, and InceptionResNetV2 has the lowest AUC value of 0.8560. / Vitiligo är en hudsjukdom där pigmentcellerna som producerar melanin dör eller slutar fungera, vilket får vita fläckar att dyka upp på kroppen. Även om Vitiligo inte betraktas som en allvarlig sjukdom, det finns fortfarande risk att något är fel på en persons immun. Under de senaste åren har användningen av medicinska bildbehandlingstekniker vuxit och forskning fortsätter att utveckla nya tekniker för att analysera och bearbeta medicinska bilder. I många medicinska bildklassificeringsuppgifter har djupa konvolutionella neurala nätverk bevisat sin effektivitet, vilket innebär att den också kan fungera bra i Vitiligo klassificering. Vår studie använder fyra djupa konvolutionella neurala nätverk för att klassificera bilder av vitiligo och normal hud. De valda arkitekturerna är VGG-19, RESNEXT101, InceptionResNetV2 och Inception V3. ROC- och AUC mätvärden används för att bedöma varje modells prestanda. Dessutom undersöker författarna de ekonomiska fördelarna som denna teknik kan ge till sjukvårdssystemet och patienterna. För att träna och utvärdera CNN modellerna använder vi ett dataset som innehåller totalt 1341 bilder. Eftersom datasetet är begränsat används också 5-faldigt korsvalidering för att förbättra modellens förutsägelse. Resultaten visar att InceptionV3 uppnår bästa prestanda i klassificeringen av Vitiligo, med ett AUC -värde på 0,9111, och InceptionResNetV2 har det lägsta AUC -värdet på 0,8560.

Optical Coherence Tomography (OCT) has been around for more than 30 years and is still being continuously improved. The department of ophthalmology is a part of Sahlgrenska Hospital that heavily uses OCT for helping people with the treatment of eye diseases. They are currently facing a problem where the time to go from an OCT scan to treatment is being increased due to having an overload of patient visits every day. Since it requires a trained expert to analyze each OCT scan, the increase of patients is too overwhelming for the few experts that the department has. It is believed that the next phase of this medical field will be through the adoption of machine learning technology. This thesis has been issued by Sahlgrenska University Hospital (SUH), and they want to address the problem that ophthalmology has by introducing the use of machine learning into their workflow. This thesis aims to determine the best suited CNN through training and testing of pre-trained models and to build a tool that a model can be integrated into for use in ophthalmology. Transfer learning was used to compare three different types of pre-trained models offered by Keras, namely VGG16, InceptionResNet50V2 and ResNet50V2. They were all trained on an open dataset containing 84495 OCT images categorized into four different classes. These include the three diseases Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), drusen and normal eyes. To further improve the accuracy of the models, oversampling, undersampling, and data augmentation were applied to the training set and then tested in different variations. A web application was built using Tensorflow.js and Node.js that the best-performed model later was integrated into. The VGG16 model performed the best with only oversampling applied out of the three. It yielded an average of 95% precision, 95% recall and got a 95% F1-score. The second was the Inception model with only oversampling applied that got an average of 93% precision, 93% recall and a 93% F1-score. Last came the ResNet model with an average of 93% precision, 92% recall and a 92% F1-score. The results suggest that oversampling is the overall best technique for this given dataset. The chosen data augmentation techniques only lead to models performing marginally worse in all cases. It also suggests that pre-trained models with more parameters, such as the VGG16 model, have more feature mappings and, therefore, achieve higher accuracy. On this basis, parameters and better mappings of features should be taken into account when using pre-trained models.

Generative Adversarial Networks (GANs) is a deep learning method that has been developed for synthesizing data. One application for which it can be used for is image-to-image translations. This could prove to be valuable when training deep neural networks for image classification tasks. Two areas where deep learning methods are used are automotive vision systems and medical imaging. Automotive vision systems are expected to handle a broad range of scenarios which demand training data with a high diversity. The scenarios in the medical field are fewer but the problem is instead that it is difficult, time consuming and expensive to collect training data. This thesis evaluates different GAN models by comparing synthetic MR images produced by the models against ground truth images. A perceptual study is also performed by an expert in the field. It is shown by the study that the implemented GAN models can synthesize visually realistic MR images. It is also shown that models producing more visually realistic synthetic images not necessarily have better results in quantitative error measurements, when compared to ground truth data. Along with the investigations on medical images, the thesis explores the possibilities of generating synthetic street view images of different resolution, light and weather conditions. Different GAN models have been compared, implemented with our own adjustments, and evaluated. The results show that it is possible to create visually realistic images for different translations and image resolutions.

Stroke is an enormous global burden, six and a half-million people die fromstroke annually [1]. Effectively monitoring blood hemodynamic parameters suchas blood velocity and volume flow permits to help and cure people. This projectaimed to calibrate a custom-made wearable system for measuring cerebral bloodflow (CBF) using a photoplethysmography (PPG) sensor. The measurementswere validated using Doppler ultrasound as a reference method. Five (N=5)subjects (age = 24±1.41 years) were selected for the project. The PPG and Dopplerultrasound probe were placed above the left and right common carotid arteries(CCA), respectively. Measurements were taken simultaneously for 12 secondseach, with six consecutive measurements per subject and 2 time-synchronizedECG recordings. Subsequently, using an extraction algorithm the velocityenvelope (TAMEAN) was extracted from the Doppler image to obtain the bloodvolume flow (ml/min). After synchronization, the PPG signal output expressedin volts was calibrated to the corresponding volume, and a calibration curve wascreated.The extraction algorithm achieved remarkable results, with almost perfectcorrelation with the Doppler image reference, rT AM EAN =0.951 and rvolume=0.975demonstrating its reliability. Challenges encountered during postprocessingand synchronization highlighted the need for careful refinement in the projectframework. Despite successful signal processing and alignment techniques,calibration results were suboptimal due to synchronization difficulties andmotion artifacts. Limitations included impractical measurement locations andsusceptibility to movement artifacts. The calibration process did not yield theexpected outcomes and the project aim was not achieved. All the linear regressionmodels for each subject failed to accurately predict the volume flow based on themeasured voltages. Future work could focus on refining calibration procedures,improving synchronization methods, and expanding studies to include largercohorts. Although the wearable device was tested, the project’s goal was onlypartially achieved, underscoring the complexity of accurately measuring cerebralblood flow using PPG sensors. / Stroke är en enorm global börda, sex och en halv miljon människor dör av strokeårligen [1]. Effektiv övervakning av hemodynamiska blodparametrar såsomblodflödeshastighet och volymflöde gör det möjligt att hjälpa och bota människor.Detta projekt syftade till att kalibrera ett specialtillverkat bärbart system föratt mäta cerebralt blodflöde (CBF) med hjälp av en fotopletysmografisensor(PPG). Mätningarna validerades med Doppler-ultraljud som referensmetod. Fem(N=5) försökspersoner (ålder = 24±1.41 år) valdes ut för projektet. PPG- ochDoppler-ultraljudssonden placerades över vänster respektive höger gemensamhalsartär (CCA). Mätningar togs samtidigt i 12 sekunder vardera, med sexpå varandra följande mätningar per försöksperson och 2 tids-synkroniseradeEKG-inspelningar. Därefter användes en extraktionsalgoritm för att extraherahastighetskuvertet (TAMEAN) från Doppler-bilden för att få blodvolymflödet(ml/min). Efter synkronisering kalibrerades PPG-signalens utgång uttryckt i volttill motsvarande volym, och en kalibreringskurva skapades.Extraktionsalgoritmen uppnådde anmärkningsvärda resultat, med nästan perfektkorrelation med Doppler-bildreferensen, rT AM EAN =0.951 och rvolume=0.975,vilket visar dess tillförlitlighet. Utmaningar som uppstod under efterbearbetningoch synkronisering betonade behovet av noggrann förfining av projektetsramverk. Trots framgångsrik signalbehandling och justeringstekniker varkalibreringsresultaten suboptimala på grund av synkroniseringssvårigheteroch rörelseartefakter. Begränsningar inkluderade opraktiska mätplatser ochkänslighet för rörelseartefakter. Kalibreringsprocessen gav inte de förväntaderesultaten och projektmålet uppnåddes inte. Alla linjära regressionsmodellerför varje försöksperson misslyckades med att noggrant förutsäga volymflödetbaserat på de uppmätta spänningarna. Framtida arbete kan fokusera på att förfinakalibreringsprocedurer, förbättra synkroniseringsmetoder och utöka studier tillatt omfatta större kohorter. Även om den bärbara enheten testades, uppnåddesprojektets mål endast delvis, vilket understryker komplexiteten i att noggrantmäta cerebralt blodflöde med hjälp av PPG-sensorer.

Tesis por compendio / [ES] Según World Health Organization (WHO), el cáncer es una de las principales causas de muerte a nivel mundial, con cerca de 10 millones de fallecimientos en 2020. Esto significa que aproximadamente una de cada seis muertes es causada por el cáncer. Para prevenir y disminuir esta enorme cantidad de muertes, es necesario un diagnóstico preciso del cáncer. Las técnicas basadas en Deep Learning (DL) han ofrecido algunas técnicas en el Diagnóstico Asistido por Computadora (CAD) para ayudar a los médicos con su diagnóstico. Estas técnicas no solo disminuyen la carga de trabajo de los patólogos, sino que también aumentan la precisión de sus diagnósticos con menos costos. Las colecciones de imágenes de alta resolución, como las láminas histopatológicas y las exploraciones médicas, han mejorado el rendimiento de estas técnicas. En esta tesis, nos enfocamos principalmente en imágenes histopatológicas escaneadas por escáneres de Whole Slide Images (WSI). Estas imágenes se introducen en métodos basados en DL, que emplean Redes Neuronales Convolucionales (CNN) para detectar las anomalías y los patrones en el tejido escaneado. Estas técnicas son capaces de analizar el tejido para disminuir los impactos de los errores humanos en el diagnóstico del cáncer. Content-Based Medical Image Retrieval (CBMIR) es uno de estos métodos que recientemente ha captado la atención de los investigadores en patología digital. En esta tesis, proponemos tres marcos CBMIR sobre imágenes histopatológicas con dos técnicas basadas en DL que se presentan en diferentes escenarios. En cuanto a los obstáculos potenciales que un CBMIR en patología digital podría enfrentar, incluida la limitación de recursos de GPU, la falta de suficientes conjuntos de datos, y las estrictas regulaciones de privacidad de datos para el intercambio de datos. En relación con estas complejidades, nos enfocamos en el aprendizaje federado en la segunda clase de nuestra investigación. En esta sección, combinamos los conceptos de Federated Learning (FL) con un marco CBMIR para imitar un CBMIR Federado Mundial (FedCBMIR) en imágenes histológicas de cáncer de mama. En esta investigación, seguimos tres escenarios para imitar los tres casos de uso de FedCBMIR en el flujo de trabajo médico. En la última contribución de esta tesis, el enfoque principal es una estrategia basada en aprendizaje contrastivo. Proponemos un marco CBMIR que puede superar las técnicas anteriores con el top K (K>1) y también tener un alto rendimiento en la recuperación de imágenes en el top primero. Además, otra contribución de esta tesis es resolver los desafíos que los patólogos tienen al clasificar los Tumores Spitzoides de Potencial Maligno Incierto (STUMP). Los STUMP presentan un dilema diagnóstico debido a su intrincada histología, creando desafíos para establecer parámetros claros entre nevos benignos y melanomas potencialmente malignos. Para ayudar a los patólogos a enfrentar esta complejidad, el marco puede proporcionar parches similares al top K para ellos con sus etiquetas correspondientes. En resumen, los marcos CBMIR y CBHIR propuestos en esta tesis contribuyen al diagnóstico del cáncer de próstata, mama y piel a partir de imágenes histopatológicas mediante el uso de FEs basados en DL en diferentes escenarios. Estos no solo mejoran la precisión y la eficiencia del diagnóstico del cáncer, sino que también prometen facilitar la detección temprana y las estrategias de tratamiento personalizado. Aprovechar estos marcos en el diagnóstico actual del cáncer podría conducir en última instancia a mejores resultados para los pacientes, menores costos de atención médica y una mayor calidad de vida para las personas afectadas por el cáncer de próstata, mama y piel. Estos avances tienen el potencial de impulsar un cambio social positivo y contribuir a la lucha global contra el cáncer. / [CA] Segons l'Organització Mundial de la Salut (OMS), el càncer és una de les principals causes de mort a nivell mundial, amb prop de 10 milions de defuncions en 2020. Això significa que aproximadament una de cada sis morts és causada pel càncer. Per prevenir i disminuir aquesta enorme quantitat de morts, és necessari un diagnòstic precís del càncer. Les tècniques basades en Deep Learning (DL) han ofert algunes tècniques en el Diagnòstic Assistit per Ordinador (CAD) per ajudar els metges amb el seu diagnòstic. Aquestes tècniques no només disminueixen la càrrega de treball dels patòlegs, sinó que també augmenten la precisió dels seus diagnòstics amb menys costos. Les col·leccions d'imatges d'alta resolució, com les làmines histopatològiques i les exploracions mèdiques, han millorat el rendiment d'aquestes tècniques. En aquesta tesi, ens enfoquem principalment en imatges histopatològiques escanejades per escàners de Whole Slide Images (WSI). Aquestes imatges s'introdueixen en mètodes basats en DL, que empren Xarxes Neuronals Convolucionals (CNN) per detectar les anomalies i els patrons en el teixit escanejat. Aquestes tècniques són capaces d'analitzar el teixit per disminuir els impactes dels errors humans en el diagnòstic del càncer. El Content-Based Medical Image Retrieval (CBMIR) és un d'aquests mètodes que recentment ha captat l'atenció dels investigadors en patologia digital. En aquesta tesi, proposem tres marcs CBMIR sobre imatges histopatològiques amb dues tècniques basades en DL que es presenten en diferents escenaris. Pel que fa als obstacles potencials que un CBMIR en patologia digital podria afrontar, inclou la limitació de recursos de GPU, la manca de suficients conjunts de dades, i les estrictes regulacions de privadesa de dades per a l'intercanvi de dades. En relació amb aquestes complexitats, ens enfoquem en l'aprenentatge federat en la segona classe de la nostra investigació. En aquesta secció, combinem els conceptes de Federated Learning (FL) amb un marc CBMIR per imitar un CBMIR Federat Mundial (FedCBMIR) en imatges histològiques de càncer de mama. En aquesta investigació, seguim tres escenaris per imitar els tres casos d'ús de FedCBMIR en el flux de treball mèdic. En l'última contribució d'aquesta tesi, l'enfocament principal és una estratègia basada en aprenentatge contrastiu. Proposem un marc CBMIR que pot superar les tècniques anteriors amb el top K (K>1) i també tenir un alt rendiment en la recuperació d'imatges en el top primer. A més, una altra contribució d'aquesta tesi és resoldre els desafiaments que els patòlegs tenen a l'hora de classificar els Tumors Spitzoides de Potencial Maligne Incert (STUMP). Els STUMP presenten un dilema diagnòstic a causa de la seva intricada histologia, creant desafiaments per establir paràmetres clars entre nevus benignes i melanomes potencialment malignes. Per ajudar els patòlegs a enfrontar aquesta complexitat, el marc pot proporcionar parches similars al top K per a ells amb les seves etiquetes corresponents. En resum, els marcs CBMIR i CBHIR proposats en aquesta tesi contribueixen al diagnòstic del càncer de pròstata, mama i pell a partir d'imatges histopatològiques mitjançant l'ús de FEs basats en DL en diferents escenaris. Aquests no només milloren la precisió i l'eficiència del diagnòstic del càncer, sinó que també prometen facilitar la detecció primerenca i les estratègies de tractament personalitzat. Aprofitar aquests marcs en el diagnòstic actual del càncer podria conduir en última instància a millors resultats per als pacients, menors costos d'atenció mèdica i una major qualitat de vida per a les persones afectades pel càncer de pròstata, mama i pell. Aquests avenços tenen el potencial d'impulsar un canvi social positiu i contribuir a la lluita global contra el càncer. / [EN] According to the World Health Organization (WHO), cancer is one of the leading causes of death worldwide, with nearly 10 million deaths in 2020. This means that approximately one in six deaths is caused by cancer. To prevent and decrease this enormous number of deaths, an accurate cancer diagnosis is necessary. Deep Learning (DL)-based techniques have offered some methods in Computer-Aided Diagnosis (CAD) to assist doctors with their diagnoses. These techniques not only reduce the workload of pathologists but also increase the accuracy of their diagnoses at lower costs. Collections of high-resolution images, such as histopathological slides and medical scans, have improved the performance of these techniques. In this thesis, we focus mainly on histopathological images scanned by Whole Slide Image (WSI) scanners. These images are introduced into DL-based methods, which employ Convolutional Neural Networks (CNN) to detect anomalies and patterns in the scanned tissue. These techniques can analyze the tissue to reduce the impacts of human errors in cancer diagnosis. Content-Based Medical Image Retrieval (CBMIR) is one of these methods that has recently attracted the attention of researchers in digital pathology. In this thesis, we propose three CBMIR frameworks on histopathological images with two DL-based techniques presented in different scenarios. Regarding potential obstacles that a CBMIR in digital pathology might face, including the limitation of GPU resources, the lack of sufficient datasets, and strict data privacy regulations for data sharing. Considering these complexities, we focus on federated learning in the second part of our research. In this section, we combine the concepts of Federated Learning (FL) with a CBMIR framework to simulate a World-Wide Federated CBMIR (FedCBMIR) on histological images of breast cancer. In this research, we follow three scenarios to mimic the three use cases of FedCBMIR in the medical workflow. In the final contribution of this thesis, the main focus is a contrastive learning-based strategy. We propose a CBMIR framework that can surpass previous techniques with the top K (K>1) and also have high performance in retrieving images at the top first. Additionally, another contribution of this thesis is to solve the challenges that pathologists face in grading Spitzoid Tumors of Uncertain Malignant Potential (STUMP). STUMPs present a diagnostic dilemma due to their intricate histology, creating challenges for establishing clear parameters between benign nevi and potentially malignant melanomas. To assist pathologists in coping with this complexity, the framework can provide top K similar patches for them with their corresponding labels. In summary, the CBMIR and CBHIR frameworks proposed in this thesis contribute to the diagnosis of prostate, breast, and skin cancer from histopathological images using DL-based FEs in different scenarios. These not only improve the accuracy and efficiency of cancer diagnosis but also promise to facilitate early detection and personalized treatment strategies. Leveraging these frameworks in current cancer diagnosis could ultimately lead to better patient outcomes, lower healthcare costs, and a higher quality of life for individuals affected by prostate, breast, and skin cancer. These advances have the potential to drive positive social change and contribute to the global fight against cancer. / This study is funded by European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 860627 (CLAR- IFY Project). The work of Adrián Colomer has been supported by the ValgrAI – Valencian Graduate School and Research Network for Artificial Intelligence & Gen- eralitat Valenciana and Universitat Politècnica de València (PAID-PD-22). / Tabatabaei, Z. (2024). Deep learning strategies for histological image retrieval [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/207119 / Compendio

Αντικείμενο της παρούσας εργασία είναι ο υπολογισμός και η ταξινόμηση, με βάση το σύστημα, BIRADS της πυκνότητας του μαστού από εικόνες μαστογραφίας. Στα πλαίσια της προσπάθειας αυτής αναπτύχθηκε ολοκληρωμένο υπολογιστικό σύστημα σε γραφικό περιβάλλον ως λογισμικό πακέτο, σε γλώσσα Visual C++ .NET . Το υπολογιστικό αυτό σύστημα δέχεται σαν είσοδο εικόνες μαστογραφίας σε οποιοδήποτε από τα δημοφιλή bitmap format εικόνων όπως jpeg και tiff καθώς και DICOM αρχεία. Η λειτουργία του μπορεί να χωριστεί σε τρία στάδια: το στάδιο της προεπεξεργασίας, το στάδιο απομόνωσης της περιοχής του μαστού και το στάδιο καθορισμού της πυκνότητας του μαστού. Στο πρώτο στάδιο παρέχονται μια σειρά από στοιχειώδη εργαλεία επεξεργασίας εικόνας όπως εργαλεία περιστροφής, αποκοπής και αλλαγής αντίθεσης . Επιπρόσθετα παρέχεται η δυνατότητα Ανισοτροπικού Φιλτραρίσματος της εικόνας. Στο δεύτερο στάδιο γίνεται η απομόνωση της περιοχής του μαστού είτε απευθείας από τον χρήστη είτε αυτόματα με χρήση των ιδιοτήτων του μονογονικού (monogenic) σήματος για την αφαίρεση του παρασκηνίου (background) καθώς και κυματιδίων Gabor για τον διαχωρισμού του θωρακικού μυός. Στο τρίτο στάδιο παρέχεται η δυνατότητα ταξινόμησης της πυκνότητας του μαστού από τον χρήστη με τον καθορισμό κατάλληλου κατωφλίου των επιπέδων γκρίζου της εικόνας αλλά και η δυνατότητα αυτόματης ταξινόμησης της πυκνότητας του μαστού κατά BIRADS με χρήση Δομικών Στοιχείων Υφής (textons) και της τεχνικής pLSA. Όλες οι παραπάνω λειτουργίες παρέχονται μέσω μίας κατά το δυνατόν φιλικότερης προς τον χρήστη διεπαφής. / The present thesis aims at the classification of breast tissue according to BIRADS system based on texture features. To this end an integrated software system was developed in visual C ++. The system takes as inputs pictures in most of the popular bitmap formats like .jpeg and .till as well as DICOM. The functionality of the system is provided by three modules: (a) pre-processing module, (b) breast segmentation module and (c) the breast tissue density classification module. In the pre-processing module a set tools for image manipulation (rotation, crop, gray level adjustment) are available which are accompanied by the ability to perform anisotropic filtering to the input image. In the second module, the user has the ability to interactively define the actual borders of the breast or ask the system to perform it automatically. Automatic segmentation is a two step procedure; in the first step breast tissue is separated from its background by using the characteristics of monogenic signals, while in the second step the pectoral muscle region is subtracted using Gabor wavelets. In the density classification module the user can either ask for a calculation of breast density based on user-defined grey level threshold or perform an automatic BIRADS-based classification using texture characteristics in conjunction with Probabilistic Latent Semantic Analysis (pLSA) algorithm. Special emphasis was given to the development of a functional and user-friendly interface.