Confident Learning-Based Label Correction for Retinal Image Segmentation by Tanatorn Pethmunee, Supaporn Kansomkeat, Patama Bhurayanontachai, Sathit Intajag

“…<b>Background/Objectives:</b> In automatic medical image analysis, particularly for diabetic retinopathy, the accuracy of labeled data is crucial, as label noise can significantly complicate the analysis and lead to diagnostic errors. To tackle the issue of label noise in retinal image segmentation, an innovative label correction framework is introduced that combines Confident Learning (CL) with a human-in-the-loop re-annotation process to meticulously detect and rectify pixel-level labeling inaccuracies. …”
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A weakly-supervised follicle segmentation method in ultrasound images by Guanyu Liu, Weihong Huang, Yanping Li, Qiong Zhang, Jing Fu, Hongying Tang, Jia Huang, Zhongteng Zhang, Lei Zhang, Yu Wang, Jianzhong Hu

“…By leveraging Multiple Instance Learning (MIL), we formulated the learning process in a weakly supervised manner and developed an end-to-end trainable model that efficiently addresses the issue of annotation scarcity. Furthermore, the WSFS can be used as a prompt proposal to enhance the performance of the Segmentation Anything Model (SAM), a well-known pre-trained segmentation model utilizing few-shot learning strategies. …”
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Intracorneal Ring Segments in Keratoconus: A Narrative Literature Review by Hayat UK, Shiwani HA, Memon D, Walkden A

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Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models by Su Myat Thwin, Hyun-Seok Park

“…This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. …”
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An efficient camouflaged image segmentation with modified UNet and attention techniques by Isha Padhy, Prabhat Dansena, Sampa Sahoo, Rahul Priyadarshi

“…Traditional models, including the standard UNet architecture, struggle with this task due to ambiguous object boundaries, texture similarity between object and background, and over-segmentation or under-segmentation caused by redundant skip connections. …”
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Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation by Jing Wang, Chia S. Lim

“…Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. …”
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Specifics of investing in different segments of the financial market of the Russian economy by S. A. Anesyants

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Evaluating segmentation methods for UAV-Based Spoil Pile Delineation by Sureka Thiruchittampalam, Bikram Pratap Banerjee, Nancy F Glenn, Simit Raval

“…Among the diverse segmentation approaches evaluated, the morphology-based deep learning segmentation approach, Segment Anything Model (SAM), exhibited superior performance compared to other approaches. …”
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An Improved Northern Goshawk Optimization Algorithm for Mural Image Segmentation by Jianfeng Wang, Zuowen Bao, Hao Dong

“…To alleviate the aforementioned issues, this paper proposes a mural image segmentation algorithm based on OPBNGO by integrating the Northern Goshawk Optimization (NGO) algorithm with the off-center learning strategy, partitioned learning strategy, and Bernstein-weighted learning strategy. …”
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An improved multi-object instance segmentation based on deep learning by Nawaf Alshdaifat, Mohd Azam Osman, Abdullah Zawawi Talib

“…However, given the difficulties in adopting object detection and the instance segmentation approach, this study aims to develop an approach to overcome these issues by proposing a new approach based on the recent DL approach in addition to developing an approach for object instance segmentation. …”
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Efficient Segmentation Using Attention-Fusion Modules With Dense Predictions by Serdar Erisen

“…This approach also enhances the performance of state-of-the-art segmentation networks, addressing the challenges issued by foundation models like InternImage. …”
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Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments by Rong Xie, Chunjun Chen

“…However, dynamic detection in these segments has consistently encountered issues with long-wavelength vertical irregularities exceeding tolerance limits. …”
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Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) by Alok Bhattarai, Jan Meyer, Laura Petersilie, Syed I. Shah, Louis A. Neu, Christine R. Rose, Ghanim Ullah

“…This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. …”
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Semantic segmentation of underwater images based on the improved SegFormer by Bowei Chen, Bowei Chen, Wei Zhao, Wei Zhao, Qiusheng Zhang, Mingliang Li, Mingyang Qi, You Tang, You Tang, You Tang

“…Nevertheless, given the complexity and variability of the underwater environment, improving model accuracy remains a key challenge in underwater image segmentation tasks. To address these issues, this study presents a high-performance semantic segmentation approach for underwater images based on the standard SegFormer model. …”
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Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation by Fang Liu, YanDuo Zhang, Tao Lu, Jiaming Wang, LiWei Wang

“…Abstract Fusing multimodal data play a crucial role in accurate brain tumor segmentation network and clinical diagnosis, especially in scenarios with incomplete multimodal data. …”
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Multi-module UNet++ for colon cancer histopathological image segmentation by Qi Liu, Zhenfeng Zhao, Yingbo Wu, Siqi Wu, Yutong He, Haibin Wang, Shenwen Wang

“…Abstract In the pathological diagnosis of colorectal cancer, the precise segmentation of glandular and cellular contours serves as the fundamental basis for achieving accurate clinical diagnosis. …”
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Optimal Res-UNET architecture with deep supervision for tumor segmentation by Rahman Maqsood, Fazeel Abid, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed, Onur Osman, Shtwai Alsubai

“…BackgroundBrain tumor segmentation is critical in medical imaging due to its significance in accurate diagnosis and treatment planning. …”
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Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms by Yang Chen, Tao Yang, Shuai Dong, Like Wang, Bida Pei, Yunlong Wang

“…The results demonstrate that the proposed segmentation network achieves superior performance in pixel-level crack segmentation, with <i>Dice</i> scores of 66.2%, 54.2%, and 86.8% and <i>mIoU</i> values of 74.4%, 67.5%, and 87.9% on the SCD, CFD, and DeepCrack datasets, respectively. …”
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Segmentation and particle size analysis of coal particles based on ISUNet by Deqiang CHENG, Rui ZHANG, Tongxi XIE, Jingjing LIU, Lijuan ZHENG, Qiqi KOU, He JIANG

“…In the process of digital image segmentation, global information and edge details play a crucial role and directly affect the accuracy of the segmentation results. …”
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Update on recent hot issues and progress of acute kidney injury by Liang Xin-ling, Chen Yuan-han, Wu Yan-hua

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