Paper Citation Record · LEDGER

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset

As of 13 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2412.13884.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.13884 v1

Coverage vector

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Reference resolution

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External citation measurements

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Outbound references

Observation 312b1daa-911a-4f73-a9b0-61198ec667a9 · outbound

This paper cites Epi- demiology of fractures in children and adolescents,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Epi- demiology of fractures in children and adolescents,

Reference 1

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This paper cites Most frequently missed fractures in the emergency department,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Most frequently missed fractures in the emergency department,

Reference 2

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This paper cites Overlooked extremity fractures in the emergency department,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Overlooked extremity fractures in the emergency department,

Reference 3

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This paper cites Artificial intelligence solutions for analysis of x-ray images,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Artificial intelligence solutions for analysis of x-ray images,

Reference 4

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This paper cites Enhancing wrist abnormality detection with yolo: Analysis of state-of-the-art single-stage detection models,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Enhancing wrist abnormality detection with yolo: Analysis of state-of-the-art single-stage detection models,

Reference 5

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Unresolved cited work

Reference 6

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This paper cites A Novel Plug-in Module for Fine-Grained Visual Classification.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset A Novel Plug-in Module for Fine-Grained Visual Classification

Reference 7

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This paper cites A pediatric wrist trauma x-ray dataset (grazpedwri-dx) for machine learning,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset A pediatric wrist trauma x-ray dataset (grazpedwri-dx) for machine learning,

Reference 8

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This paper cites Arm fracture de- tection in x-rays based on improved deep convolutional neural network,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Arm fracture de- tection in x-rays based on improved deep convolutional neural network,

Reference 9

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Observation e9adf247-24c1-4708-989a-b4718df8c27e · outbound

This paper cites Musculoskeletal images classification for detection of fractures using transfer learning,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Musculoskeletal images classification for detection of fractures using transfer learning,

Reference 10

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This paper cites Parallelnet: Multiple backbone network for detection tasks on thigh bone fracture,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Parallelnet: Multiple backbone network for detection tasks on thigh bone fracture,

Reference 11

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This paper cites Detection and localization of hand fractures based on ga faster r-cnn,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Detection and localization of hand fractures based on ga faster r-cnn,

Reference 12

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This paper cites Critical eval- uation of deep neural networks for wrist fracture detection,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Critical eval- uation of deep neural networks for wrist fracture detection,

Reference 13

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Observation 50fcdb91-2ad7-4e6c-9baa-abf67419f79b · outbound

This paper cites Bone fracture detection through the two-stage system of cracksensitive convolutional neural network,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Bone fracture detection through the two-stage system of cracksensitive convolutional neural network,

Reference 14

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This paper cites Application of convolutional neural networks for distal radio-ulnar fracture detection on plain radiographs in the emergency room,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Application of convolutional neural networks for distal radio-ulnar fracture detection on plain radiographs in the emergency room,

Reference 15

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Observation fed4a9d4-0de1-4be6-91da-bed747472860 · outbound

This paper cites Deep learning-based localization and segmentation of wrist fractures on x-ray radiographs,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Deep learning-based localization and segmentation of wrist fractures on x-ray radiographs,

Reference 16

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This paper cites Fracture detection in wrist x- ray images using deep learning-based object detection models,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Fracture detection in wrist x- ray images using deep learning-based object detection models,

Reference 17

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This paper cites Fracture recognition in paediatric wrist radiographs: An object detection approach,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Fracture recognition in paediatric wrist radiographs: An object detection approach,

Reference 18

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This paper cites Symbolic Discovery of Optimization Algorithms.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Symbolic Discovery of Optimization Algorithms

Reference 19

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Observation ec09b560-bfcf-4e03-b39c-b8e31d8c0420 · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 20

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This paper cites Efficientnetv2: Smaller models and faster training,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Efficientnetv2: Smaller models and faster training,

Reference 21

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 22

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 23

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 24

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset RegNet: Self-Regulated Network for Image Classification

Reference 25

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Densely Connected Convolutional Networks

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 27

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Rexnet: Diminishing representa- tional bottleneck on convolutional neural network,

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Deep Residual Learning for Image Recognition

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset ResNeSt: Split-Attention Networks

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This paper cites Going Deeper with Convolutions.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Going Deeper with Convolutions

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This paper cites [Online].

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset [Online]

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization

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This paper cites Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification,

Reference 34

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder,

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Fine-grained visual classification via internal ensemble learning transformer,

Reference 36

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset TransFG: A Transformer Architecture for Fine-grained Recognition

Reference 37

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This paper cites MetaFormer: A Unified Meta Framework for Fine-Grained Recognition.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset MetaFormer: A Unified Meta Framework for Fine-Grained Recognition

Reference 38

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Feature Fusion Vision Transformer for Fine-Grained Visual Categorization

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Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

Reference 41

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This paper cites Reject rate analysis in digital radiography: an australian emergency imaging department case study,.

Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset Reject rate analysis in digital radiography: an australian emergency imaging department case study,

Reference 42

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Pith citing papers

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