Characteristic Appearance Research Articles

Cow detection and tracking system utilizing multi-feature tracking algorithm

In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance.

Personal Beauty and Personal Agency

AbstractWe make choices about our own appearance and evaluate others' choices – every day. These choices are meaningful for us as individuals and as members of communities. But many features of personal appearance are due to luck, and many cultural beauty standards make some groups and individuals worse off (this is called “lookism”). So, how are we to square these two facets of personal appearance? And how are we to evaluate agency in the context of personal beauty? I identify three ways of responding to these questions: beauty advocacy, beauty skepticism, and beauty revisionism. Advocates connect an honorific sense of beauty with personal character. Skeptics focus on beauty standards, and primarily offer a social critique of beauty standards. Some skeptics suggest embracing other aesthetic ideals – even ugliness. Revisionists critique beauty standards, but retain an honorific sense of beauty. Each position offers tools to evaluate personal agency as aesthetic agency, whether first‐personally or through our appreciation of others' appearance.

Seven new species of Tetranemertes Chernyshev, 1992 (Monostilifera, Hoplonemertea, Nemertea) from the Caribbean Sea, western Pacific, and Arabian Sea, and revision of the genus.

The marine ribbon worm genus Tetranemertes Chernyshev, 1992 currently includes three species: the type species T.antonina (Quatrefages, 1846) from the Mediterranean Sea, T.rubrolineata (Kirsteuer, 1965) from Madagascar, and T.hermaphroditica (Gibson, 1982) from Australia. Seven new species are described: T.bifrostsp. nov., T.ocelatasp. nov., T.majinbuuisp. nov., and T.pastafariensissp. nov. from the Caribbean Sea (Panamá), and three species, T.unistriatasp. nov., T.paulayisp. nov., and T.arabicasp. nov., from the Indo-West Pacific (Japan and Oman). As a result, an amended morphological diagnosis of the genus is offered. To improve nomenclatural stability, a neotype of Tetranemertesantonina is designated from the Mediterranean. The newly described species, each characterized by features of external appearance and stylet apparatus, as well as by DNA-barcodes, form a well-supported clade with T.antonina on a molecular phylogeny of monostiliferan hoplonemerteans based on partial sequences of COI, 16S rRNA, 18S rRNA, and 28S rRNA. Six of the seven newly described species, as well as T.rubrolineata, possess the unusual character of having a central stylet basis slightly bilobed to deeply forked posteriorly in fully grown individuals, a possible morphological synapomorphy of the genus. In addition, an undescribed species of Tetranemertes is reported from the Eastern Tropical Pacific (Panamá), increasing the total number of known species in the genus to eleven.

FRI366 “Carotid Body Tumor” Is A Misnomer For Head And Neck Paraganglioma

Abstract Disclosure: S. Rabiei: None. V. Kantorovich: None. Introduction: Glomus tumors are often treated as benign, hormonally inactive tumors through simple surgical resection. In many cases, full endocrine work-up and post-resection surveillance are overlooked. Endocrine consultation is currently underutilized in patients with glomus tumors because features of paragangliomas (PGs) such as multiple tumors are not always clinically evident at the time of initial presentation. It is important to be aware that carotid body and glomus tumors should be considered a variant of head and neck paraganglioms (HNPGs) and endocrinology consultation should be requested early on to optimize patients’ outcome. Case Presentation: Patient is a 52-year-old female who presented with back pain and difficulty walking. Imaging obtained for back pain work-up revealed a 6.2 x 4.4 x 4.2 cm right paraspinal soft tissue mass at T9 level on MRI with local invasion and cortical destruction of the T9 vertebra and involvement of the spinal canal. She was also found to have a 0.2 cm solid pulmonary nodule within the right lower lobe on chest CT. The history was also significant for resection of a jugular glomus tumor 12 years prior at the age of 40. Pathology findings at the time were consistent with glomus tumor. She also reported history of glomus tumor in her sister. However, the patient did not undergo genetic study at the time of diagnosis of her primary tumor.The patient subsequently underwent T9 corpectomy. Pathology report revealed a 5-cm tumor with characteristic histologic appearance of PG with epithelioid foci growing in distinctive small nests (Zellballen pattern), separated by prominent fibrovascular stroma with moderate cellularity and with slightly elevated (approximately 5%) Ki-67. Genetic study revealed a carrier state for a pathogenic variant of SDHD (c.112C>T; p.Arg38*). Gallium-68 DOTATATE PET/CT demonstrated intense focal DOTATATE uptake at the site of previous left-sided jugular glomus tumor resection as well as small foci of uptake bilaterally in upper and mid neck. Intense DOTATATE avidity was also identified at T8, T9, and T10 levels. Serum metanephrine, catecholamine, and PTH levels were within normal range. Chromogranin A is pending at this time. Discussion: We presented a case of glomus tumor in a young patient with a positive family history for glomus tumor who did not receive genetic study upon initial diagnosis of her primary tumor. The patient presented 12 years later with metastatic thoracic PG. Considering high mortality and morbidity of PGs, it is recommended to request endocrinology consultation at the time of diagnosis of carotid body or glomus tumors to prevent negative effects of disease recurrence. Presentation: Friday, June 16, 2023

Investigation of 3D printing of toddler foods with special shape and function based on fenugreek gum and flaxseed protein

The practicability of using corn and flaxseed protein as printing inks for manufacture of printed products specifically designed for toddlers as a dysphagia diet with high precision and special shapes with addition of fenugreek gum (FGG) was investigated. 3D printing was used to process grains and dysphagia-compatible food (corn) into a dietary product with attractive appearance which was also easy to swallow. Rheological measurements shown that appropriate amount of flaxseed protein (FP, 0–10 %) can reduce the stickiness and yield strength of printing material. Based on FTIR measurements, FP weakened the hydrogen bond strength of inks, but it was still an important gradient for the formation of the ink suitable for precision 3D printing. The TPA results shown that the addition of FP (0–10 %) remarkably reduced both the stickiness and hardness of the ink. These results shown that compared with the control group, materials with FGG additions possessed higher printing accuracy and self-supporting ability. Ink with 5 % FP content exhibited the best printability and swallowability, while ink with 10 % FP content had the lowest viscosity and hardness, but it was not suitable for 3D printing. 3D printing of objects printed using Ink-C (5%FP and 0.8 %FGG) showed high support characteristic and attractive appearance. According to the international IDDSI testing standards, Ink-C (5%FP and 0.8 %FGG), Ink-E (15%FP and 0.8 %FGG), and Ink-F (20%FP and 0.8 %FGG) were defined as level 5-minced and moist foods.

Graph-Based Facial Affect Analysis: A Review

As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. This paper provides a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their non-deep or deep learning methods, emphasizing the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.

Juvenile ossifying fibroma of maxilla: Concurrent presentation of psammomatoid and trabecular variant.

Juvenile Ossifying Fibroma (JOF) is a type of ossifying fibroma which occurs in younger individuals and manifests as trabecular and psammomatoid variants. The nature and behaviour of these variants vary, and they exhibit characteristic histopathological appearance. The solitary presentation of these subtypes is reported in numbers, but co-occurrence of both these entities is very few. Here, we present a case of JOF with the co-occurrence of both trabecular and psammomatoid variants in relation to an incompletely healed extraction socket.

Vulvar dermatofibrosarcoma of Darier and Ferrand in a young woman: About a new observation

Darier and Ferrand Dermatofibrosarcoma is a rare mesenchymal tumor in young adults, often located in the trunk. We report an observation of a vulvar localization of dermatofibrosarcoma Darier and Ferrand. It was a young patient, aged 25, who presented a tumor of the vulvar region. This tumor has been evolving for about 10 years in two phases, the first of which is longer, with very slow development, and a second phase characterized by rapid growth of the tumor coinciding with the start of pregnancy. The examination noted a tumor developed at the expense of the vulvar region. The diagnosis of dermatofibrosarcoma was retained in view of a characteristic histopathological appearance. The treatment consisted of resection surgery with margins of about 5 cm followed by reconstruction by direct suture. The evolution, in the immediate aftermath, was good. Vulvar localization of dermatofibrosarcoma is possible. Key words: Darier and Ferrand dermatofibrosarcoma, Vulva, Young woman

Graph Attention Networks and Track Management for Multiple Object Tracking

Multiple object tracking (MOT) constitutes a critical research area within the field of computer vision. The creation of robust and efficient systems, which can approximate the mechanisms of human vision, is essential to enhance the efficacy of multiple object-tracking techniques. However, obstacles such as repetitive target appearances and frequent occlusions cause considerable inaccuracies or omissions in detection. Following the updating of these inaccurate observations into the tracklet, the effectiveness of the tracking model, employing appearance features, declines significantly. This paper introduces a novel method of multiple object tracking, employing graph attention networks and track management (GATM). Utilizing a graph attention network, an attention mechanism is employed to capture the relationships of nodes within the graph as well as node-to-node correlations across graphs. This mechanism allows selective focus on the features of advantageous nodes and enhances discriminability between node features, subsequently improving the performance and robustness of multiple object tracking. Simultaneously, we categorize distinct tracklet states and introduce an efficient track management method, which employs varying processing techniques for tracklets in diverse states. This method can manage occluded tracks in crowded scenes and improves tracking accuracy. Experiments conducted on three challenging public datasets (MOT16, MOT17, and MOT20) demonstrate that our method could deliver competitive performance.

RelNet-MAM: Relation Network with Multilevel Attention Mechanism for Image Captioning

Text present in an image contains rich semantic information which is crucial for the understanding of an image. For example, a signboard having the text “deep water” conveys the danger involved in the image. The current image captioning models do not effectively utilize this useful semantic information due to their limited representation capabilities of scene-text tokens. Our work presents a novel image captioning model called RelNet-MAM, which utilizes a multilevel attention mechanism and relation network. To improve the appearance feature representation, RelNet-MAM uses multilevel attention which consists of spatial attention, channel-wise attention, and semantic attention. For representing the scene-text token effectively, RelNet-MAM uses appearance, FastText, location, and PHOC features for each token. Further, the proposed RelNet-MAM uses the relation network to establish the relationships between the objects and scene-text tokens. Finally, the transformer model together with dynamic pointer networks is used as a decoder in the caption generation process. The proposed RelNet-MAM model outperforms the state-of-the-art models on TextCaps, Flickr30k, and MS COCO datasets. TextCaps requires models to read and reason about the texts in an image for caption generation. MSCOCO and Flickr30k contain diverse images; persons, animals, automobiles, indoor and outdoor scenes. Remarkably, the proposed RelNet-MAM model outperforms the current best model by 2.3% on B-4, 1.8% on METEOR, 2.2% on ROUGE-L, 2.0% on CIDEr-D and 3.0% on SPICE metric scores on TextCaps dataset.

MSGNet: multi-source guidance network for fish segmentation in underwater videos

Fish segmentation in underwater videos provides basic data for fish measurements, which is vital information that supports fish habitat monitoring and fishery resources survey. However, because of water turbidity and insufficient lighting, fish segmentation in underwater videos has low accuracy and poor robustness. Most previous work has utilized static fish appearance information while ignoring fish motion in underwater videos. Considering that motion contains more detail, this paper proposes a method that simultaneously combines appearance and motion information to guide fish segmentation in underwater videos. First, underwater videos are preprocessed to highlight fish in motion, and obtain high-quality underwater optical flow. Then, a multi-source guidance network (MSGNet) is presented to segment fish in complex underwater videos with degraded visual features. To enhance both fish appearance and motion information, a non-local-based multiple co-attention guidance module (M-CAGM) is applied in the encoder stage, in which the appearance and motion features from the intra-frame salient fish and the moving fish in video sequences are reciprocally enhanced. In addition, a feature adaptive fusion module (FAFM) is introduced in the decoder stage to avoid errors accumulated in the video sequences due to blurred fish or inaccurate optical flow. Experiments based on three publicly available datasets were designed to test the performance of the proposed model. The mean pixel accuracy (mPA) and mean intersection over union (mIoU) of MSGNet were 91.89% and 88.91% respectively with the mixed dataset. Compared with those of the advanced underwater fish segmentation and video object segmentation models, the mPA and mIoU of the proposed model significantly improved. The results showed that MSGNet achieves excellent segmentation performance in complex underwater videos and can provide an effective segmentation solution for fisheries resource assessment and ocean observation. The proposed model and code are exposed via Github1.

Enhancing vehicle localization by matching HD map with road marking detection

Accurate and robust vehicle localization, a fundamental task for autonomous driving, is still a challenging problem especially in GPS-denied scenarios. Recently, high definition (HD) map suggests a promising solution. However, the matching between online sensed data and HD map is difficult and time-consuming. In our work, road markings are selected as landmarks due to salient appearance features. Based on the detection results of road markings from the vehicle-borne images, the points of edge lines are employed to fit straight lines with RANSAC for outlier removal. The distances between the ego-vehicle and the fitted edge lines can be computed with camera-vehicle calibration in advance. Subsequently, the point-to-line distances from the sensed data are mapped into global linear constraints on the vehicle’s positions with the support of a lane-level HD map, which provides centimeter-level coordinates of road markings. Finally, the distances and the localization from the integrated navigation system (INS) are fused with the proposed linear Kalman filter based on the second-order Markov model (KF-MM2). The proposed method has been verified in different daily driving scenarios. Experimental results demonstrate that our method can achieve good performance with an average localization error of 0.53 m and a standard deviation of 0.17 m.

Magnetic Resonance Imaging Findings in Patients With Duane Retraction Syndrome.

Duane retraction syndrome (DRS) is known to relate to the absence of the abducens nucleus, with abnormal innervation of the lateral rectus (LR) muscle by branchesof the oculomotor nerve (CN III). The purposes of this study were to investigate the morphological characteristics of the oculomotor nerve (CN III), the abducens nerve (CN VI), and the extraocular muscles in patients with clinically diagnosed Duane retraction syndrome (DRS) using MRI. In addition, we assessed the association between ocular motility, horizontal rectus muscle volumes, and CN III/VI in patients with Duane retraction syndrome (DRS). The study comprised 20 orthotropic control subjects (40 eyes) and 42 patients with Duane syndrome (48 eyes), including 20 patients with DRS Type I (24 eyes), 5 patients with DRS Type II (6 eyes), and 17 patients with DRS Type III (18 eyes). Three-dimensional (3D) T1/2 images of the brainstem and orbit were obtained to visualize the cranial nerves, especially the abducens (VI) and oculomotor (III) nerves, as well as extraocular muscles. Based on the clinical classification, among 42 patients, MRI showed that the abducens nerves (CN VI) on the affected side were absent in 24 of 24 eyes (100%; 20 patients) with Type I DRS and in 16 of 18 eyes (88%; 16 patients) with Type III DRS. However, CN VI was observed in 6 of 6 eyes (100%; 5 patients) with Type II DRS and in 2 of 18 eyes (11%) with Type III DRS. CN III was observed in all patients. The oculomotor nerves on the affected side were thicker than those on the nonaffected contralateral side in DRS Type I ( P < 0.05) and Type III ( P < 0.05), but not in DRS Type II. Smaller LR and larger MR volumes were shown in the affected eye than that in the nonaffected eye in DRS Types I and III. Based on the presence or absence of CN VI, there was a tendency for thicker oculomotor nerves in the affected eye than in the nonaffected eye in the absence groups ( P < 0.05). However, no significant difference was found in the present group. In the CN VI absence groups, similar results were found in the affected eyes than in the nonaffected eyes as in DRS Types I and III. In addition, the presence of CN VI was correlated with better abduction ( P = 0.008). The LR and MR volumes have positive correlations with the oculomotor nerve diameter in the affected eye. However, there was no correlation between the range of adduction/abduction and the LR/MR ratio in patients with or without an abducens nerve. Different types of DRS have different characteristic appearances of CN VI and CN III on MRI. Horizontal rectus muscles have morphological changes to adapt to dysinnervation of CN VI and aberrant innervation of CN III. Thus, these neuroimaging findings may provide a new diagnostic criterion for the classification of DRS, improving the comprehension of the physiopathogenics of this disease.

A Generative Adversarial Network with Spatial Attention Mechanism for Building Structure Inference Based on Unmanned Aerial Vehicle Remote Sensing Images

The acquisition of building structures has broad applications across various fields. However, existing methods for inferring building structures predominantly depend on manual expertise, lacking sufficient automation. To tackle this challenge, we propose a building structure inference network that utilizes UAV remote sensing images, with the PIX2PIX network serving as the foundational framework. We enhance the generator by incorporating an additive attention module that performs multi-scale feature fusion, enabling the combination of features from diverse spatial resolutions of the feature map. This modification enhances the model’s capability to emphasize global relationships during the mapping process. To ensure the completeness of line elements in the generator’s output, we design a novel loss function based on the Hough transform. A line penalty term is introduced that transforms the output of the generator and ground truth to the Hough domain due to the original loss function’s inability to effectively constrain the completeness of straight-line elements in the generated results in the spatial domain. A dataset of the appearance features obtained from UAV remote sensing images and the internal floor plan structure is made. Using UAV remote sensing images of multi-story residential buildings, high-rise residential buildings, and office buildings as test collections, the experimental results show that our method has better performance in inferring a room’s layout and the locations of load-bearing columns, achieving an average improvement of 11.2% and 21.1% over PIX2PIX in terms of the IoU and RMSE, respectively.

Spatio-Temporal Adaptive Network With Bidirectional Temporal Difference for Action Recognition

Action Recognition is a fundamental task in computer vision field, with a wide range of applications in autonomous driving, security monitoring, etc. However, previous action recognition approaches usually suffer from the inappropriate spatio-temporal modeling or high computational consumption (e.g., 3D CNN). In this paper, we propose a novel Spatio-Temporal Adaptive Network (STANet) with bidirectional temporal difference, consisting of a Temporal Adaptive module (TA) and a Spatial Adaptive (SA) module, to sufficiently extract the crucial motion information and model the spatial pivotal appearance information from both forward and backward perspectives, respectively. Specifically, the Temporal Adaptive module uses bidirectional temporal differences to learn valuable motion trends and balance the static semantics and dynamic motion for a certain action during information fusion; while the Spatial Adaptive module uses the bidirectional temporal difference to obtain the spatio-channel attention to stress the discriminative position-relevant and semantic-relevant appearance features. Extensive experiments conducted on widely-used action recognition benchmarks UCF-101, HMDB-51, Something-Something V1, and Kinetics-400 prove the effectiveness of the proposed methods compared to other state-of-the-art approaches.

Improve multi-object tracking using yolov5 and deep-sort for Chinese yam counting model

&lt;p&gt;Accurate counting information is important for crop yield and quality. Obtaining yield estimation of Chinese Yam is critical to improving productivity. Referring to the method of counting pedestrian flow in surveillance video and its accuracy, a method for counting the number of Chinese Yams harvested in the field is proposed by improving YOLOv5s detection combined with Deep-SORT tracking. In order to improve the recognition effect of the detector, the attention module CBAM is fused with the Neck part of the YOLOv5s network to improve the feature extraction ability of the network; CIoU Loss is used instead of GIoU Loss as the target bounding box regression loss function to speed up the bounding box regression rate while improving positioning Accuracy; use DIoU-NMS to replace NMS to improve the missed detection problem when the target is crowded. Adjust the structure of the Deep SORT appearance feature extraction network and retrain on the yam re-identification dataset to reduce the identity switching caused by target occlusion. Connect the improved YOLOv5s detector and Deep SORT, and set a virtual detection line in the video to count the number of yams. The experimental results show that the number of yams can be counted more accurately. Compared with the original algorithm, the improved YOLOv5s has an average accuracy rate of 2.1 percentage points. Combined with Deep-SORT tracking, the counting statistics accuracy rate reaches 92.7%.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Diagnosis and Management Strategies in Sclerochoroidal Calcification: A Systematic Review.

Sclerochoroidal calcification (SCC) is a rare disease which is characterized by calcium deposition in the sclera. The choroid is secondarily involved. Typical localization is in the midperipheral region, outside the vascular arcades. SCC is mostly located in the superotemporal quadrant. Often times, the patients are referred with the diagnosis of an amelanotic tumor. SCC may be dystrophic or metastatic. Metastatic SCC lesions are associated with conditions altering calcium and phosphate metabolism including primary and secondary hyperparathyroidism, vitamin D intoxication, renal failure, hyperphosphatemia, and destructive bony lesions. SCC lesions have a characteristic appearance and appear as distinct, ill-defined, yellow-white, elevated scleral/choroidal masses funduscopically. The purpose of this literature review is to review the current knowledge on SCC, highlight the imaging features, and discuss the differential diagnosis as well as management options.

Pycnodysostosis: In the Indian Population – A Case Report with Review of the Literature

Abstract Bone disorders having different causes, such as metabolic, genetic, and environmental, are well known and each of them has a characteristic clinical and radiographic appearance. Pycnodysostosis is one such rare bone disease that manifests as generalized osteosclerosis of the skeleton because of decreased bone turnover. Some of the unique radiographic and clinical features distinguish it from other bone disorders, such as osteogenesis imperfect, osteopetrosis, and cleidocranial dysplasia. Recognition of these signs is important to make a diagnosis and prevent further complications. We report a case of a 24-year-old male, who presented with a chief complaint of dirty and malaligned teeth and was unhappy with his facial appearance. General examination features which drew attention were short stature, beaking of the nose, and frontal and parietal bossing. Radiographically, open fontanelles, wormian bones, and dental abnormalities were evident.

Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity.

Visual object tracking is a fundamental task in computer vision that requires estimating the position and scale of a target object in a video sequence. However, scale variation is a difficult challenge that affects the performance and robustness of many trackers, especially those based on the discriminative correlation filter (DCF). Existing scale estimation methods based on multi-scale features are computationally expensive and degrade the real-time performance of the DCF-based tracker, especially in scenarios with restricted computing power. In this paper, we propose a practical and efficient solution that can handle scale changes without using multi-scale features and can be combined with any DCF-based tracker as a plug-in module. We use color name (CN) features and a salient feature to reduce the target appearance model's dimensionality. We then estimate the target scale based on a Gaussian distribution model and introduce global and local scale consistency assumptions to restore the target's scale. We fuse the tracking results with the DCF-based tracker to obtain the new position and scale of the target. We evaluate our method on the benchmark dataset Temple Color 128 and compare it with some popular trackers. Our method achieves competitive accuracy and robustness while significantly reducing the computational cost.

IgG4-Related Disease, a Systemic or Local Disease: This is the Problem

Background: Immunoglobulin G4-related disease (IgG4-RD) is a fibroinflammatory condition that forms tumefactive lesions in various organs, presenting with diverse clinical manifestations. Diagnosis of IgG4-RD could be challenging and relies on characteristic histopathological findings and elevated IgG4+ plasma cell counts. Case Presentation: The patient was a 48-year-old woman with diabetes mellitus initially presented with a painless breast mass. Imaging indicated irregular opacities and lymph nodes in the breast. Biopsy results revealed lymphoplasmacytic infiltration with IgG and IgG4 positivity, overall compatible with IG4-related disease. A mass resection was performed, and intraoperative assessment ruled out malignancy. Then, with a 5-month interval of no symptoms, the patient developed a cheek mass, initially misdiagnosed as low-grade spindle cell sarcoma. Subsequently, she underwent partial maxillectomy and further examination and histology confirmed IgG4-RD, meeting all diagnostic criteria. Conclusion: IgG4-RD is a rare, systemic fibroinflammatory condition with potential to mimic neoplastic processes. While IgG4 serum levels can be elevated, they are not a reliable diagnostic criterion. The two main features of IgG-related disease are an elevated number of IgG4-positive plasma cells within the tissue and a characteristic histopathological appearance including a dense lymphoplasmacytic infiltrate, storiform fibrosis, and obliterative phlebitis. Overall, diagnosis is based on the balance of clinical features, histopathology, and serum markers. This case underscores the diagnostic challenges posed by IgG4-RD, often mistaken for malignancies due to its invasive behavior and emphasizes the importance of considering IgG4-RD in differential diagnoses for tumor-like lesions, particularly when affecting multiple organs, to enable timely treatment decisions and prevent unnecessary interventions.