METHODS: In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using the pre-trained CNNs and lastly perform the deep ensemble learning to determine the final classification. The heterogeneous ensemble models consisted of CNN models from five deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet. These CNN models were fine-tuned and used as base learners, they exhibited different performances and had great diversity in histopathological image analysis. The CNN models with superior classification accuracy (Acc) were then selected to undergo ensemble learning for the final classification. The performance of the investigated ensemble approaches was evaluated against the state-of-the-art literature.

RESULTS: The performance evaluation demonstrated the superiority of the proposed best performing ensembled model: five-CNN based weighted averaging model, with an Acc (99%), specificity (Sp) (98%), F1-score (F1) (99%) and area under the receiver operating characteristic (ROC) curve (98%) but slightly inferior recall (Re) (99%) compared to the literature.

METHODS: Diffusion-weighted imaging, perfusion-weighted imaging, and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) data were acquired from five rats that underwent scans at 9.4 T after middle cerebral artery occlusion.

RESULTS: The apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and apparent exchange-dependent relaxations (AREX) at 3.5 ppm and NOE(-1.6 ppm) were quantified. AREX(3.5 ppm) and NOE(-1.6 ppm) were found to be hypointense and exhibited different signal patterns within the ischemic tissue. The NOE(-1.6 ppm) deficit areas were equal to or larger than the ADC deficit areas, but smaller than the AREX(3.5 ppm) deficit areas. This suggested that NOE(-1.6 ppm) might further delineate the acidotic tissue estimated using AREX(3.5 ppm). Since NOE(-1.6 ppm) is closely related to membrane phospholipids, NOE(-1.6 ppm) potentially highlighted at-risk tissue affected by lipid peroxidation and membrane damage. Altogether, the ADC/NOE(-1.6 ppm)/AREX(3.5 ppm)/CBF mismatches revealed four zones of increasing sizes within the ischemic tissue, potentially reflecting different pathophysiological information.

METHODS: Three testing datasets were included. All imaging data were acquired at 3.0T. Dataset-1 consisted of 16 HGs (lesion diameter: 1.5-8.85 cm), 4 focal nodular hyperplasia (FNHs, lesion diameter: 1.72-5.7 cm), and 24 HCCs (lesion diameter: 1.83-12.77 cm), and DDVDm was reconstructed with b=0 and b=2 images. Dataset-2 consisted of 6 HGs (lesion diameter: 1.14-6.2 cm), and DDVDm was reconstructed with b=0 and b=10 images. Dataset-3 consisted of 28 HCCs (lesion diameter: 1.91-3.52 cm), and DDVDm was reconstructed with b=0 and b=2 images. For dataset-1 and dataset-2, a trained reader was required to make a diagnosis for a lesion solely based on DDVDm with 4 choices: (I) HG with confidence; (II) HG without confidence; (III) solid mass-forming lesion (MFL) with confidence; (IV) solid MFL without confidence. Then, three readers attempted to confirm whether DDVDm features summarized from dataset-1 and dataset-2 would be generalizable to dataset-3.

RESULTS: For dataset-1 and dataset-2 together, the correct diagnosis was made by the trained reader in 90.9% (20/22) of the HGs (77.7% with confidence) and 96.4% (27/28) of the MFLs (85.7% with confidence). HG generally showed substantially higher DDVD signal relative to background liver parenchyma. Though not necessarily, HG DDVD signals could be similar to those of blood vessels. Some HGs showed DDVD signals higher or similar to that of kidneys which have a higher perfusion than the liver. MFL generally showed DDVD signals only slightly higher, similar to, or even slightly lower, than that of background liver parenchyma. The DDVDm features of dataset-3 were all consistent with MFL.

METHODS: In this study, we developed ensemble models based on deep convolutional neural networks (CNNs) for the classification of colorectal cancer histopathology images. The method first involved data preprocessing techniques such as patch cropping, stain normalization, data augmentation and data balancing on histopathology images with different magnifications. Subsequently, the CNN models were fine-tuned and pre-trained using transfer learning methods, and models with superior performance were then selected as the base classifiers to build the ensemble models. Finally, the ensemble models were used to predict the final classification outcomes. To evaluate the effectiveness of the proposed models, we tested their performance on a publicly available colorectal cancer dataset, Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image (EBHI) dataset.

RESULTS: Experimental results show that the proposed ensemble model, composed of the top five classifiers, achieved the promising classification accuracy across sub-databases with four different magnification factors. Specifically, on the 40× magnification subset, the highest classification accuracy reached 99.11%; on the 100× magnification subset, it reached 99.36%; on the 200× magnification subset, it was 99.29%; and on the 400× magnification subset, it was 98.96%. Additionally, the proposed ensemble model achieved exceptional results in recall, precision, and F1 score.