2024
Authors
Neto, PC; Montezuma, D; Oliveira, SP; Oliveira, D; Fraga, J; Monteiro, A; Monteiro, J; Ribeiro, L; Gonçalves, S; Reinhard, S; Zlobec, I; Pinto, IM; Cardoso, JS;
Publication
NPJ PRECISION ONCOLOGY
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
2024
Authors
Pinheiro, MR; Fernandes, LE; Carneiro, IC; Carvalho, SD; Henrique, RM; Tuchin, VV; Oliveira, HP; Oliveira, LM;
Publication
JOURNAL OF BIOPHOTONICS
Abstract
With the objective of developing new methods to acquire diagnostic information, the reconstruction of the broadband absorption coefficient spectra (mu a[lambda]) of healthy and chromophobe renal cell carcinoma kidney tissues was performed. By performing a weighted sum of the absorption spectra of proteins, DNA, oxygenated, and deoxygenated hemoglobin, lipids, water, melanin, and lipofuscin, it was possible to obtain a good match of the experimental mu a(lambda) of both kidney conditions. The weights used in those reconstructions were estimated using the least squares method, and assuming a total water content of 77% in both kidney tissues, it was possible to calculate the concentrations of the other tissue components. It has been shown that with the development of cancer, the concentrations of proteins, DNA, oxygenated hemoglobin, lipids, and lipofuscin increase, and the concentration of melanin decreases. Future studies based on minimally invasive spectral measurements will allow cancer diagnosis using the proposed approach.
2024
Authors
Coelho, BFO; Nunes, SLP; de França, CA; Costa, DdS; do Carmo, RF; Prates, RM; Filho, EFS; Ramos, RP;
Publication
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Abstract
2024
Authors
Souza, C; Viana, G; Coelho, B; Massaranduba, AB; Ramos, R;
Publication
Anais do XVI Congresso Brasileiro de Inteligência Computacional
Abstract
2023
Authors
Queirós, R; Ruela, J; Fontes, H; Campos, R;
Publication
CoRR
Abstract
2023
Authors
Pereira, A; Carvalho, P; Pereira, N; Viana, P; Corte-Real, L;
Publication
IEEE ACCESS
Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.