Article
Multidisciplinary Sciences
Jun Ruan, Zhikui Zhu, Chenchen Wu, Guanglu Ye, Jingfan Zhou, Junqiu Yue
Summary: With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has gained clinical attention, impacting the working style of pathologists. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images, achieving excellent performance in pixel-level detection with improved methods and models.
Article
Engineering, Biomedical
Emrah Hancer, Mohamed Traore, Refik Samet, Zeynep Yildirim, Nooshin Nemati
Summary: A key step in computational pathology is to automate the manual nuclei segmentation process in H&E stained whole slide images. This paper introduces an imbalance-aware nuclei segmentation methodology that outperforms recent studies in terms of evaluation metrics on histopathology images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Automation & Control Systems
Valerio Biscione, Jeffrey S. Bowers
Summary: This study tested various CNN architectures and found that, apart from DenseNet-121, none of the tested models were architecturally invariant to translation, although they could learn this invariance. By pretraining, using simpler datasets, and avoiding catastrophic forgetting/interference, translation invariance can be achieved.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Editorial Material
Computer Science, Interdisciplinary Applications
Adrien Foucart, Olivier Debeir, Christine Decaestecker
Summary: The MoNuSAC 2020 challenge hosted at the ISBI 2020 conference has been analyzed, revealing three problems in the computation of the metric used for ranking. The incorrect code version was used to rank the algorithms in the challenge. The results can be replicated using the code provided on GitHub.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Artificial Intelligence
Talha Ilyas, Zubaer Ibna Mannan, Abbas Khan, Sami Azam, Hyongsuk Kim, Friso De Boer
Summary: Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task. This paper proposes a method based on tissue specific feature distillation (TSFD) backbone and utilizes a bi-directional feature pyramid network (BiFPN) to generate a robust hierarchical feature pyramid. Extensive ablation studies validate the effectiveness of the method, and it outperforms other methods on the PanNuke dataset.
Article
Oncology
Rashadul Islam Sumon, Subrata Bhattacharjee, Yeong-Byn Hwang, Hafizur Rahman, Hee-Cheol Kim, Wi-Sun Ryu, Dong Min Kim, Nam-Hoon Cho, Heung-Kook Choi
Summary: This study aimed to develop a deep learning-based method for nuclei segmentation in histological images for computational pathology. The proposed technique outperformed other methods and achieved superior nuclei segmentation with high accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores.
FRONTIERS IN ONCOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Syed Nauyan Rashid, Muhammad Moazam Fraz
Summary: Nuclei instance segmentation is crucial for various downstream tasks in digital pathology. However, there are challenges such as staining variation, overlapping nuclei, and limited labeled data. In this paper, a lightweight and state-of-the-art NC-Net model is proposed to address these challenges, achieving accurate and fast nuclei instance segmentation.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yibao Sun, Xingru Huang, Huiyu Zhou, Qianni Zhang
Summary: The proposed similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images utilize an embedding layer for similarity learning to enhance classification performance. Experimental results demonstrate that networks applying similarity learning outperform conventional methods on both tasks of multi-organ nuclei detection and signet ring cells detection.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoxuan Zhang, Xiongfeng Zhu, Kai Tang, Yinghua Zhao, Zixiao Lu, Qianjin Feng
Summary: In this paper, a novel dense dual-task network (DDTNet) is proposed to achieve automatic detection and segmentation of tumor-infiltrating lymphocytes (TILs) in histopathological images. DDTNet utilizes a feature pyramid network for extracting multi-scale morphological characteristics of TILs, a detection module for locating TIL centers, and a segmentation module for delineating TIL boundaries. Experimental results show that DDTNet outperforms other methods in detection and segmentation metrics.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Interdisciplinary Applications
Hans Pinckaers, Wouter Bulten, Jeroen van der Laak, Geert Litjens
Summary: Prostate cancer is the most prevalent cancer among men in Western countries, and pathologists' evaluation is the gold standard for diagnosis. State-of-the-art convolutional neural networks are often patch-based and require detailed pixel-level annotations for effective training.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Jun Zhang, Zhiyuan Hua, Kezhou Yan, Kuan Tian, Jianhua Yao, Eryun Liu, Mingxia Liu, Xiao Han
Summary: This paper introduces a weakly-supervised model using joint fully convolutional and graph convolutional networks for automated segmentation of pathology images. By utilizing image-level labels instead of pixel-wise annotations, the segmentation model's performance is improved. Experimental results demonstrate the effectiveness of this method in cancer region segmentation.
MEDICAL IMAGE ANALYSIS
(2021)
Review
Biochemistry & Molecular Biology
Jeroen van der Laak, Geert Litjens, Francesco Ciompi
Summary: Recent advancements in machine learning have shown great potential to enhance medical diagnostics, particularly in the field of histopathology. However, challenges remain in implementing these techniques in clinical settings.
Article
Engineering, Biomedical
Hameed Ullah Khan, Basit Raza, Munawar Hussain Shah, Syed Muhammad Usama, Prayag Tiwari, Shahab S. Band
Summary: Breast cancer is a common and deadly disease caused by uncontrolled cell proliferation. Artificial intelligence can assist with early and accurate diagnosis, improving survival rates. The proposed SMDetector model uses dilated layers to detect small objects like mitotic nuclei, achieving better results than existing standard models. It achieves an average precision of 68.49% and an average recall of 59.86% for mitotic nuclei on the ICPR 2014 dataset.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Oncology
Sung Hak Lee, Yujin Lee, Hyun-Jong Jang
Summary: This study attempted a fully automated classification of MSI status in gastric cancer tissue slides using deep learning. The DL-based MSI classifier showed good performance on both frozen and FFPE tissues, demonstrating its potential as a screening tool.
INTERNATIONAL JOURNAL OF CANCER
(2023)
Article
Oncology
Sung Hak Lee, In Hye Song, Hyun-Jong Jang
Summary: The study developed a DL-based MSI classifier for predicting MSI status in colorectal cancer tissues, demonstrating its potential as a screening tool.
INTERNATIONAL JOURNAL OF CANCER
(2021)
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)