Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 321 - 334

Опубликована: Янв. 1, 2025

Язык: Английский

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

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Content-Based Histopathological Image Retrieval

Sensors, Год журнала: 2025, Номер 25(5), С. 1350 - 1350

Опубликована: Фев. 22, 2025

Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, essential tool to support pathologists. Deep learning models like Convolutional Neural Networks and Vision Transformers improve extraction these feature descriptors. These typically generate embeddings by leveraging deeper single-scale linear layers or advanced pooling layers. However, embeddings, focusing on local spatial details at a single scale, miss out richer context from earlier This gap suggests development methods that incorporate multi-scale information enhance depth utility image analysis. In this work, we propose Local–Global Fusion Embedding Model. proposal is composed three elements: (1) pre-trained backbone multi-scales, (2) neck branch local–global fusion, (3) Generalized Mean (GeM)-based head Based our experiments, model’s were trained ImageNet-1k PanNuke datasets employing Sub-center ArcFace loss compared with state-of-the-art Kimia Path24C dataset retrieval, achieving Recall@1 99.40% test patches.

Язык: Английский

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Pathology: Diagnostics, Reporting and Artificial Intelligence

Senologie - Zeitschrift für Mammadiagnostik und -therapie, Год журнала: 2025, Номер 22(01), С. 31 - 42

Опубликована: Март 1, 2025

Abstract Breast pathology poses a particular diagnostic challenge due to the broad spectrum of functional, reactive and neoplastic changes in breast. Objectifiable reproducible criteria are key valid diagnosis. In addition classification lesions, it is task pathologists identify document all tumor characteristics that relevant for clinical management. Modern personalized medicine based on up-to-date, pathomorphological molecular diagnostics. Reports findings should be written comprehensibly, completely quickly. Structured reports ideal this purpose. Before artificial intelligence can fulfil hopes placed regarding acceleration objectification reporting, technical financial limitations must resolved explainability AI-generated decisions.

Язык: Английский

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Meme Kanseri Segmentasyonu için Topluluk Öğrenmesine Dayalı LinkNet Modeli

Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, Год журнала: 2025, Номер 9(1), С. 63 - 74

Опубликована: Март 27, 2025

Meme kanseri birçok ülkede kadınlar arasında en sık görülen kanser türüdür. kanserinin tanı ve tedavisinde verilerin analizi büyük bir önem taşımaktadır. Histopatolojik görüntülerdeki kanserli hücre çekirdeklerinin segmentasyonu, uzmanlar için oldukça maliyetli zorlu iştir. Bu çalışmada, histopatolojik meme görüntülerinin çekirdek segmentasyonu topluluk öğrenmesine dayalı LinkNet modeli önerilmektedir. Görüntüler, Kontrast Sınırlı Adaptif Histogram Eşitleme (CLAHE) tekniği ile işlendikten sonra veri artırma uygulanır. modelinin kodlayıcı kısmında ResNeXT50 Vgg19 modellerinin yerleştirildiği iki ayrı model eğitilir. Sonrasında, bu modeller öğrenmesi birleştirilir maske tahmini yapılır. Çalışmada elde edilen 0.702 Kümülatif Jaccard İndeks (AJI) metriği sonucu, aynı seti yapılmış son çalışmalardan daha başarılı bulunmuştur.

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Artificial intelligence in cancer pathology: Applications, challenges, and future directions

CytoJournal, Год журнала: 2025, Номер 22, С. 45 - 45

Опубликована: Апрель 19, 2025

The application of artificial intelligence (AI) in cancer pathology has shown significant potential to enhance diagnostic accuracy, streamline workflows, and support precision oncology. This review examines the current applications AI across various types, including breast, lung, prostate, colorectal cancer, where aids tissue classification, mutation detection, prognostic predictions. key technologies driving these advancements include machine learning, deep computer vision, which enable automated analysis histopathological images multi-modal data integration. Despite promising developments, challenges persist, ensuring privacy, improving model interpretability, meeting regulatory standards. Furthermore, this explores future directions AI-driven pathology, real-time diagnostics, explainable AI, global accessibility, emphasizing importance collaboration between pathologists. Addressing leveraging AI's full could lead a more efficient, equitable, personalized approach care.

Язык: Английский

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PSPNet EPO-SEB: a novel attention-enhanced hybrid model for accurate histopathological image segmentation

Connection Science, Год журнала: 2025, Номер 37(1)

Опубликована: Май 24, 2025

Язык: Английский

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An unsupervised transfer learning model based on convolutional auto encoder for non-alcoholic steatohepatitis activity scoring and fibrosis staging of liver histopathological images

Neural Computing and Applications, Год журнала: 2023, Номер 35(14), С. 10605 - 10619

Опубликована: Янв. 27, 2023

Язык: Английский

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Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma

IEEE Access, Год журнала: 2024, Номер 12, С. 37557 - 37571

Опубликована: Янв. 1, 2024

Viral and non-viral hepatocellular carcinoma (HCC) is becoming predominant in developing countries. A major issue linked to HCC-related mortality rate the late diagnosis of cancer development. Although traditional approaches diagnosing HCC have become gold-standard, there remain several limitations due which confirmation progression takes a longer period. The recent emergence artificial intelligence tools with capacity analyze biomedical datasets assisting diagnostic for early certainty. Here we present review versus use (Machine Learning Deep Learning) diagnosis. overview cancer-related databases along AI histopathology, radiology, biomarker, electronic health records (EHRs) based given.

Язык: Английский

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Performance of artificial intelligence versus clinicians on the detection of contact between mandibular third molar and inferior alveolar nerve

Oral Science International, Год журнала: 2024, Номер 22(1)

Опубликована: Май 19, 2024

Abstract Purpose This study aimed to assess the performance of ResNet‐50 deep learning algorithm in classifying panoramic images determine contact between mandibular third molars and inferior alveolar nerve (IAN), comparing its with newly graduated dentists oral maxillofacial surgery specialists. Methods Panoramic radiographs were retrieved from Radiology Department, School Dentistry, Kashan University Medical Sciences. The independently classified as “contact” or “none‐contact” by model, three dentists, A radiologist sets gold standard using cone beam‐CT. accuracy, precision, recall, specificity, dice coefficient calculated for each group, inter‐rater reliability assessed Cohen's kappa value. Results Of 548 retrieved, 15% allocated testing dataset, amounting 82 images. model showed highest metrics, an accuracy 87.80%, precision 78.57%, recall 84.61%, specificity 89.28%, 81.48%. Conversely, novice had lowest metrics (accuracy: 74.39% ± 2.99%, precision: 57.75% 4.33%, recall: 74.36% 1.81%, specificity: 74.4% 4.46%, coefficient: 64.87% 2.69%). Specialists demonstrated 84.96% 1.52%, 72.65% 2.78%, 84.61% 3.14%, 85.12% 2.23%, 78.11% 1.99%. Conclusion Deep algorithms can achieve comparable outcomes specialists may outperform clinicians diagnosing IAN.

Язык: Английский

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