Kaggle医学影像新赛事：腹部创伤检测

def dicom_to_image(dicom_image):    """    Read the dicom file and preprocess appropriately.    """    pixel_array = dicom_image.pixel_array        if dicom_image.PixelRepresentation == 1:        bit_shift = dicom_image.BitsAllocated - dicom_image.BitsStored        dtype = pixel_array.dtype         new_array = (pixel_array << bit_shift).astype(dtype) >>  bit_shift        pixel_array = pydicom.pixel_data_handlers.util.apply_modality_lut(new_array, dicom_image)        if dicom_image.PhotometricInterpretation == "MONOCHROME1":        pixel_array = 1 - pixel_array        # transform to hounsfield units    intercept = dicom_image.RescaleIntercept    slope = dicom_image.RescaleSlope    pixel_array = pixel_array * slope + intercept        # windowing    window_center = int(dicom_image.WindowCenter)    window_width = int(dicom_image.WindowWidth)    img_min = window_center - window_width // 2    img_max = window_center + window_width // 2    pixel_array = pixel_array.copy()    pixel_array[pixel_array < img_min] = img_min    pixel_array[pixel_array > img_max] = img_max        # normalization    pixel_array = (pixel_array - pixel_array.min())/(pixel_array.max() - pixel_array.min())        return (pixel_array * 255).astype(np.uint8)

model = torchvision.models.resnet34(True)model.fc = torch.nn.Linear(512, 14)model.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
model = model.cuda()pos_weight = torch.Tensor([1,2,1,6,1,2,4,1,2,4,1,2,4,6]).cuda()criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight)optimizer = torch.optim.SGD(model.parameters(), 0.0001)

pred_list = []for pid in train_pids[:10]:        pid_paths_dcm_paths = glob.glob('/kaggle/input/rsna-2023-abdominal-trauma-detection/train_images/' + str(pid) + '/*/*')    pid_paths_dcm_paths.sort()        pid_paths_dcm_paths = pid_paths_dcm_paths[-5:]    imgs = [Image.fromarray(dicom_to_image(pydicom.read_file(x))) for x in pid_paths_dcm_paths]    imgs = [transform(x) for x in imgs]        imgs = torch.cat(imgs, 0)    imgs = imgs[:, None, :, :]    with torch.no_grad():        imgs = imgs.cuda()        output = model(imgs)[:, :-1]        pred = torch.sigmoid(output).data.cpu().numpy().round(3)        pred = pred.mean(0)            pred_list.append(pred)