XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification DOI Creative Commons
Asmaa Abbas, Mohamed Medhat Gaber, Mohammed M. Abdelsamea

и другие.

Sensors, Год журнала: 2022, Номер 22(24), С. 9875 - 9875

Опубликована: Дек. 15, 2022

Of the various tumour types, colorectal cancer and brain tumours are still considered among most serious deadly diseases in world. Therefore, many researchers interested improving accuracy reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised has been proven to be an effective solution when dealing with datasets insufficient data annotations. However, image often suffer from irregularities, making recognition task even more challenging. The class decomposition approach provided a robust such challenging problem by simplifying boundaries dataset. this paper, we propose model, called XDecompo, improve transferability features pretext downstream task. XDecompo designed based on affinity propagation-based effectively encourage explainable component highlight important pixels that contribute classification explain effect speciality extracted features. We also explore generalisability handling different datasets, as histopathology for images. quantitative results demonstrate robustness high 96.16% 94.30% CRC images, respectively. demonstrated its generalization capability achieved (both quantitatively qualitatively) compared other Moreover, post hoc method used validate feature transferability, demonstrating highly accurate representations.

Язык: Английский

Content-Based Histopathological Image Retrieval DOI Creative Commons
Camilo Núñez, Humberto Farías, Mauricio Solar

и другие.

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

Процитировано

0
Pathology: Diagnostics, Reporting and Artificial Intelligence DOI Creative Commons

Annette Lebeau,

Andreas Turzynski

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

Процитировано

0
Meme Kanseri Segmentasyonu için Topluluk Öğrenmesine Dayalı LinkNet Modeli DOI Open Access
Furkan Atlan

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.

Процитировано

0
Artificial intelligence in cancer pathology: Applications, challenges, and future directions DOI Open Access

Jiu-Le Wang,

Teng Wang, Rui Han

и другие.

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

Процитировано

0
PSPNet EPO-SEB: a novel attention-enhanced hybrid model for accurate histopathological image segmentation DOI Creative Commons

Prem Purusottam Jena,

Debahuti Mishra, Kaberi Das

и другие.

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

Опубликована: Май 24, 2025

Язык: Английский

Процитировано

0
Amplifying Human Capabilities in Prostate Cancer Diagnosis: An Empirical Study of Current Practices and AI Potentials in Radiology DOI Creative Commons
Sheree May Saßmannshausen, Nazmun Nisat Ontika, Aparecido Fabiano Pinatti de Carvalho

и другие.

Опубликована: Май 11, 2024

This paper examines the potential of Human-Centered AI (HCAI) solutions to support radiologists in diagnosing prostate cancer. Prostate cancer is one most prevalent and increasing cancers among men. The scarcity raises concerns about their ability address growing demand for diagnosis, leading a significant surge workload radiologists. Drawing on an HCAI approach, we sought understand current practices concerning radiologists' work detecting cancer, as well challenges they face. findings from our empirical studies point toward that has expedite informed decision-making enhance accuracy, efficiency, consistency. particularly beneficial collaborative diagnosis processes. We discuss these results introduce design recommendations concepts domain with aim amplifying professional capabilities

Язык: Английский

Процитировано

3
PSO-SLIC algorithm: A novel automated method for the generation of high-homogeneity slope units using DEM data DOI
Yange Li,

Bangjie Fu,

Zheng Han

и другие.

Geomorphology, Год журнала: 2024, Номер 463, С. 109367 - 109367

Опубликована: Июль 31, 2024

Язык: Английский

Процитировано

3
An unsupervised transfer learning model based on convolutional auto encoder for non-alcoholic steatohepatitis activity scoring and fibrosis staging of liver histopathological images DOI
Meryem Altın Karagöz, Bahriye Akay, Alper Baştürk

и другие.

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

Опубликована: Янв. 27, 2023

Язык: Английский

Процитировано

7
Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma DOI Creative Commons
Arun Asif, Faheem Ahmed, Zeeshan Saifi

и другие.

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

Процитировано

2
Performance of artificial intelligence versus clinicians on the detection of contact between mandibular third molar and inferior alveolar nerve DOI
Amir Yari, Paniz Fasih, Atieh Nouralishahi

и другие.

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

Процитировано

2