Deep Learning Approaches in Histopathology

Ahmed, Alhassan Ali; Abouzid, Mohamed; Kaczmarek, Elżbieta

doi:10.3390/cancers14215264

Cited by 23 publications

(13 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have tested the known and unknown data with our developed models and as a result all models detect aromatase-related proteins accurately. In future studies, we would like to work on the aromatase inhibitors with molecular docking, and we are also interested in using a deep learning technique [80][81][82]. We believe that this study will facilitate researchers in finding new or undiscovered aromatase-related proteins.…”

Section: Discussionmentioning

confidence: 99%

Computational method for aromatase-related proteins using machine learning approach

Selvaraj

Kaur

2023

PLoS ONE

View full text Add to dashboard Cite

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

show abstract

Section: Discussionmentioning

confidence: 99%

Computational method for aromatase-related proteins using machine learning approach

Selvaraj

Kaur

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…81 These promising data notwithstanding, it is important to remember that the implementation of DIA algorithms in routine clinical practice is still far from being achieved, with further studies needed to validate standard models, define image storage policies and assess the pathologist's responsibilities. 88…”

Section: Methods Of Artificial Intelligence In Diagnosisymentioning

confidence: 99%

Updates on lung neuroendocrine neoplasm classification

Vocino Trucco,

Righi,

Volante

et al. 2023

Histopathology

View full text Add to dashboard Cite

Lung neuroendocrine neoplasms (NENs) are a heterogeneous group of pulmonary neoplasms showing different morphological patterns and clinical and biological characteristics. The World Health Organisation (WHO) classification of lung NENs has been recently updated as part of the broader attempt to uniform the classification of NENs. This much‐needed update has come at a time when insights from seminal molecular characterisation studies revolutionised our understanding of the biological and pathological architecture of lung NENs, paving the way for the development of novel diagnostic techniques, prognostic factors and therapeutic approaches. In this challenging and rapidly evolving landscape, the relevance of the 2021 WHO classification has been recently questioned, particularly in terms of its morphology‐orientated approach and its prognostic implications. Here, we provide a state‐of‐the‐art review on the contemporary understanding of pulmonary NEN morphology and the potential contribution of artificial intelligence, the advances in NEN molecular profiling with their impact on the classification system and, finally, the key current and upcoming prognostic factors.

show abstract

“…3 Majority voting and pseudo-labeling are two common DL methods for histopathology WSI classification. 4 The majority voting splits WSIs into small non-overlapping tiles, and assigns each tile with the same WSI-level label to train the classification model. This tiling strategy reduces the computational burden.…”

Section: Introductionmentioning

confidence: 99%

Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images

Zhang,

Fan,

Wang

et al. 2024

Medical Imaging 2024: Image Processing

View full text Add to dashboard Cite

Histopathology whole-slide image (WSI) captures detailed structural and morphological features of tumor tissue, offering rich histological and molecular information to support clinical practice. With the development of artificial intelligence, deep learning (DL) methods have emerged to assist in automatically analyzing histopathology WSIs. It alleviates the need for tedious, time-consuming, and error-prone inspections by clinicians. Up to now, employing DL models for histopathology WSI analysis is still challenging due to the intrinsic complexity of histology characteristics of tumor tissue, high image resolution, and large image size. In this study, we proposed a transformer-based classifier with feature aggregation for cancer subtype classification using histopathology WSIs while addressing these challenges. Our method shows three advantages to improve classification performance. First, an aggregate transformer decoder is employed to learn both global and local features from WSIs. Second, the transformer architecture facilitates the decoder to learn spatial correlations among different regions in a WSI. Third, the self-attention mechanism of the transformer facilitates the generation of saliency maps to highlight regions of interest in WSIs. We evaluated our model on three cancer subtype classification tasks and demonstrated its effectiveness and performance.

show abstract

Deep Learning Approaches in Histopathology

Cited by 23 publications

References 94 publications

Computational method for aromatase-related proteins using machine learning approach

Computational method for aromatase-related proteins using machine learning approach

Updates on lung neuroendocrine neoplasm classification

Transformer-based classifier with feature aggregation for cancer subtype classification on histopathological images

Contact Info

Product

Resources

About